Bio

Bio


Shenoy conducts basic and applied research on neural prosthetic systems. Basic studies include investigating sensory-motor and cognitive functions in the primate cortex using a combination of behavioral, electrophysiological, and computational techniques to discover how populations of neurons represent movement plans. Applied studies include designing algorithms to read out these representations and developing prosthetic systems controlled by the neural activity. The ultimate goal of these neural prosthetic systems, or brain-computer interfaces, is to assist disabled patients.

Academic Appointments


Administrative Appointments


  • Leadership Council, Bio-X (2011 - Present)
  • Executive Committee, SINTN (2009 - 2013)
  • Co-Director, Neural Prosthetics Translational Laboratory (2009 - Present)
  • Director, Neural Prosthetic Systems Laboratory (2001 - Present)

Honors & Awards


  • Senior Member, IEEE (2013)
  • Fellow, Defense Science Research Council (DARPA) (2013)
  • Distinguished Alumnus Award, The Henry Samueli School of Engineering, University of California at Irvine (2013)
  • Postdoc Mentoring Award, Stanford University (2010)
  • NIH Director's Pioneer Award, National Institutes of Health (2009)
  • Charles Lee Powell Faculty Scholar, School of Engineering, Stanford University (2008)
  • Technological Innovations in Neurosciences Award, McKnight Foundation (2007)
  • Research Fellow, Alfred P. Sloan Foundation (2002)
  • Career Award in the Biomedical Sciences, Burroughs Wellcome Fund (1999)

Boards, Advisory Committees, Professional Organizations


  • Senior Member, IEEE Engineering in Medicine and Biology (EMBS) (2013 - Present)
  • Member, Society for Neuroscience (2013 - Present)
  • Member, American Physiological Society (2013 - Present)
  • Member, Neural Control of Movement Society (2013 - Present)

Professional Education


  • Professor, Stanford University, Dept. of Electrical Engineering; Dept. of Neurobiology (by courtesy); Dept. of Bioengineering (affiliate); BioX & Neurosciences Graduate Programs; Stanford Neurosciences Institute (2012)
  • Associate Professor, Stanford University, Dept. of Electrical Engineering; Dept. of Bioengineering (affiliate); BioX & Neurosciences Graduate Programs; Stanford Institute for Neuro-Innovation and Translational Neuroscience (2008)
  • Assistant Professor, Stanford University, Dept. of Electrical Engineering; BioX & Neurosciences Graduate Programs (2001)
  • Postdoc, Caltech, Neurobiology, Senior Postdoc, 1998-2001; Neurobiology, Postdoc, 1995-1998 (1998)
  • Ph.D., MIT, Electrical Engineering (1995)
  • S.M., MIT, Electrical Engineering (1992)
  • B.S., University of California, Irvine, Electrical Engineering (1990)

Research & Scholarship

Current Research and Scholarly Interests


Prof. Shenoy heads the Neural Prosthetic Systems Lab (NPSL) at Stanford University (www.stanford.edu/~shenoy/Group.htm) where his group conducts neuroscience and neuroengineering research to better understand how the brain controls movement, and to design medical systems to assist those with movement disabilities. His neuroscience (systems and cognitive neuroscience) research investigates the neural basis of movement preparation and generation using a combination of electrophysiological (single-electrode and chronic electrode-array recordings in rhesus monkeys), behavioral, computational and theoretical techniques. His neuroengineering (electrical, bio, and biomedical engineering) research investigates the design of high-performance neural prosthetic systems, which are also known as brain-computer interfaces (BCIs) and brain-machine interfaces (BMIs). These systems translate neural activity from the brain into control signals for prosthetic devices, which assist disabled patients by restoring lost function. This work includes statistical signal processing, machine learning, low-power circuits, and real-time system modeling and implementation. Prof. Shenoy also Co-Directs (along with Co-Director Prof. Jaimie Henderson), Neural Prosthetics Translational Laboratory (NPTL), part of Stanford Institute for Neuro-Innovation and Translational Neuroscience (SINTN) and Stanford's Bio-X / NeuroVentures program

Clinical Trials


  • BrainGate2: Feasibility Study of an Intracortical Neural Interface System for Persons With Tetraplegia Recruiting

    The purpose of this study is to obtain preliminary device safety information and demonstrate proof of principle (feasibility of efficacy) of the ability of people with tetraplegia to control a computer cursor and other assistive devices with their thoughts.

    View full details

Teaching

Publications

Journal Articles


  • Cortical activity in the null space: permitting preparation without movement. Nature neuroscience Kaufman, M. T., Churchland, M. M., Ryu, S. I., Shenoy, K. V. 2014; 17 (3): 440-448

    Abstract

    Neural circuits must perform computations and then selectively output the results to other circuits. Yet synapses do not change radically at millisecond timescales. A key question then is: how is communication between neural circuits controlled? In motor control, brain areas directly involved in driving movement are active well before movement begins. Muscle activity is some readout of neural activity, yet it remains largely unchanged during preparation. Here we find that during preparation, while the monkey holds still, changes in motor cortical activity cancel out at the level of these population readouts. Motor cortex can thereby prepare the movement without prematurely causing it. Further, we found evidence that this mechanism also operates in dorsal premotor cortex, largely accounting for how preparatory activity is attenuated in primary motor cortex. Selective use of 'output-null' vs. 'output-potent' patterns of activity may thus help control communication to the muscles and between these brain areas.

    View details for DOI 10.1038/nn.3643

    View details for PubMedID 24487233

  • Context-dependent computation by recurrent dynamics in prefrontal cortex NATURE Mante, V., Sussillo, D., Shenoy, K. V., Newsome, W. T. 2013; 503 (7474): 78-?

    Abstract

    Prefrontal cortex is thought to have a fundamental role in flexible, context-dependent behaviour, but the exact nature of the computations underlying this role remains largely unknown. In particular, individual prefrontal neurons often generate remarkably complex responses that defy deep understanding of their contribution to behaviour. Here we study prefrontal cortex activity in macaque monkeys trained to flexibly select and integrate noisy sensory inputs towards a choice. We find that the observed complexity and functional roles of single neurons are readily understood in the framework of a dynamical process unfolding at the level of the population. The population dynamics can be reproduced by a trained recurrent neural network, which suggests a previously unknown mechanism for selection and integration of task-relevant inputs. This mechanism indicates that selection and integration are two aspects of a single dynamical process unfolding within the same prefrontal circuits, and potentially provides a novel, general framework for understanding context-dependent computations.

    View details for DOI 10.1038/nature12742

    View details for Web of Science ID 000326585600035

    View details for PubMedID 24201281

  • Cortical control of arm movements: a dynamical systems perspective. Annual review of neuroscience Shenoy, K. V., Sahani, M., Churchland, M. M. 2013; 36: 337-359

    Abstract

    Our ability to move is central to everyday life. Investigating the neural control of movement in general, and the cortical control of volitional arm movements in particular, has been a major research focus in recent decades. Studies have involved primarily either attempts to account for single-neuron responses in terms of tuning for movement parameters or attempts to decode movement parameters from populations of tuned neurons. Even though this focus on encoding and decoding has led to many seminal advances, it has not produced an agreed-upon conceptual framework. Interest in understanding the underlying neural dynamics has recently increased, leading to questions such as how does the current population response determine the future population response, and to what purpose? We review how a dynamical systems perspective may help us understand why neural activity evolves the way it does, how neural activity relates to movement parameters, and how a unified conceptual framework may result. Expected final online publication date for the Annual Review of Neuroscience Volume 36 is July 08, 2013. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.

    View details for DOI 10.1146/annurev-neuro-062111-150509

    View details for PubMedID 23725001

  • A high-performance neural prosthesis enabled by control algorithm design NATURE NEUROSCIENCE Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Churchland, M. M., Kaufman, M. T., Kao, J. C., Ryu, S. I., Shenoy, K. V. 2012; 15 (12): 1752-1757

    Abstract

    Neural prostheses translate neural activity from the brain into control signals for guiding prosthetic devices, such as computer cursors and robotic limbs, and thus offer individuals with disabilities greater interaction with the world. However, relatively low performance remains a critical barrier to successful clinical translation; current neural prostheses are considerably slower, with less accurate control, than the native arm. Here we present a new control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF) that incorporates assumptions about the nature of closed-loop neural prosthetic control. When tested in rhesus monkeys implanted with motor cortical electrode arrays, the ReFIT-KF algorithm outperformed existing neural prosthetic algorithms in all measured domains and halved target acquisition time. This control algorithm permits sustained, uninterrupted use for hours and generalizes to more challenging tasks without retraining. Using this algorithm, we demonstrate repeatable high performance for years after implantation in two monkeys, thereby increasing the clinical viability of neural prostheses.

    View details for DOI 10.1038/nn.3265

    View details for Web of Science ID 000311706700023

    View details for PubMedID 23160043

  • Neural population dynamics during reaching NATURE Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Foster, J. D., Nuyujukian, P., Ryu, S. I., Shenoy, K. V. 2012; 487 (7405): 51-?

    Abstract

    Most theories of motor cortex have assumed that neural activity represents movement parameters. This view derives from what is known about primary visual cortex, where neural activity represents patterns of light. Yet it is unclear how well the analogy between motor and visual cortex holds. Single-neuron responses in motor cortex are complex, and there is marked disagreement regarding which movement parameters are represented. A better analogy might be with other motor systems, where a common principle is rhythmic neural activity. Here we find that motor cortex responses during reaching contain a brief but strong oscillatory component, something quite unexpected for a non-periodic behaviour. Oscillation amplitude and phase followed naturally from the preparatory state, suggesting a mechanistic role for preparatory neural activity. These results demonstrate an unexpected yet surprisingly simple structure in the population response. This underlying structure explains many of the confusing features of individual neural responses.

    View details for DOI 10.1038/nature11129

    View details for Web of Science ID 000305982900048

    View details for PubMedID 22722855

  • Single-Trial Neural Correlates of Arm Movement Preparation NEURON Afshar, A., Santhanam, G., Yu, B. M., Ryu, S. I., Sahani, M., Shenoy, K. V. 2011; 71 (3): 555-564

    Abstract

    The process by which neural circuitry in the brain plans and executes movements is not well understood. Until recently, most available data were limited either to single-neuron electrophysiological recordings or to measures of aggregate field or metabolism. Neither approach reveals how individual neurons' activities are coordinated within the population, and thus inferences about how the neural circuit forms a motor plan for an upcoming movement have been indirect. Here we build on recent advances in the measurement and description of population activity to frame and test an "initial condition hypothesis" of arm movement preparation and initiation. This hypothesis leads to a model in which the timing of movements may be predicted on each trial using neurons' moment-by-moment firing rates and rates of change of those rates. Using simultaneous microelectrode array recordings from premotor cortex of monkeys performing delayed-reach movements, we compare such single-trial predictions to those of other theories. We show that our model can explain approximately 4-fold more arm-movement reaction-time variance than the best alternative method. Thus, the initial condition hypothesis elucidates a view of the relationship between single-trial preparatory neural population dynamics and single-trial behavior.

    View details for DOI 10.1016/j.neuron.2011.05.047

    View details for Web of Science ID 000293991700017

    View details for PubMedID 21835350

  • An optogenetic toolbox designed for primates NATURE NEUROSCIENCE Diester, I., Kaufman, M. T., Mogri, M., Pashaie, R., Goo, W., Yizhar, O., Ramakrishnan, C., Deisseroth, K., Shenoy, K. V. 2011; 14 (3): 387-397

    Abstract

    Optogenetics is a technique for controlling subpopulations of neurons in the intact brain using light. This technique has the potential to enhance basic systems neuroscience research and to inform the mechanisms and treatment of brain injury and disease. Before launching large-scale primate studies, the method needs to be further characterized and adapted for use in the primate brain. We assessed the safety and efficiency of two viral vector systems (lentivirus and adeno-associated virus), two human promoters (human synapsin (hSyn) and human thymocyte-1 (hThy-1)) and three excitatory and inhibitory mammalian codon-optimized opsins (channelrhodopsin-2, enhanced Natronomonas pharaonis halorhodopsin and the step-function opsin), which we characterized electrophysiologically, histologically and behaviorally in rhesus monkeys (Macaca mulatta). We also introduced a new device for measuring in vivo fluorescence over time, allowing minimally invasive assessment of construct expression in the intact brain. We present a set of optogenetic tools designed for optogenetic experiments in the non-human primate brain.

    View details for DOI 10.1038/nn.2749

    View details for Web of Science ID 000287650100021

    View details for PubMedID 21278729

  • Cortical Preparatory Activity: Representation of Movement or First Cog in a Dynamical Machine? NEURON Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S. I., Shenoy, K. V. 2010; 68 (3): 387-400

    Abstract

    The motor cortices are active during both movement and movement preparation. A common assumption is that preparatory activity constitutes a subthreshold form of movement activity: a neuron active during rightward movements becomes modestly active during preparation of a rightward movement. We asked whether this pattern of activity is, in fact, observed. We found that it was not: at the level of a single neuron, preparatory tuning was weakly correlated with movement-period tuning. Yet, somewhat paradoxically, preparatory tuning could be captured by a preferred direction in an abstract "space" that described the population-level pattern of movement activity. In fact, this relationship accounted for preparatory responses better than did traditional tuning models. These results are expected if preparatory activity provides the initial state of a dynamical system whose evolution produces movement activity. Our results thus suggest that preparatory activity may not represent specific factors, and may instead play a more mechanistic role.

    View details for DOI 10.1016/j.neuron.2010.09.015

    View details for Web of Science ID 000284255800009

    View details for PubMedID 21040842

  • Stimulus onset quenches neural variability: a widespread cortical phenomenon NATURE NEUROSCIENCE Churchland, M. M., Yu, B. M., Cunningham, J. P., Sugrue, L. P., Cohen, M. R., Corrado, G. S., Newsome, W. T., Clark, A. M., Hosseini, P., Scott, B. B., Bradley, D. C., Smith, M. A., Kohn, A., Movshon, J. A., Armstrong, K. M., Moore, T., Chang, S. W., Snyder, L. H., Lisberger, S. G., Priebe, N. J., Finn, I. M., Ferster, D., Ryu, S. I., Santhanam, G., Sahani, M., Shenoy, K. V. 2010; 13 (3): 369-U25

    Abstract

    Neural responses are typically characterized by computing the mean firing rate, but response variability can exist across trials. Many studies have examined the effect of a stimulus on the mean response, but few have examined the effect on response variability. We measured neural variability in 13 extracellularly recorded datasets and one intracellularly recorded dataset from seven areas spanning the four cortical lobes in monkeys and cats. In every case, stimulus onset caused a decline in neural variability. This occurred even when the stimulus produced little change in mean firing rate. The variability decline was observed in membrane potential recordings, in the spiking of individual neurons and in correlated spiking variability measured with implanted 96-electrode arrays. The variability decline was observed for all stimuli tested, regardless of whether the animal was awake, behaving or anaesthetized. This widespread variability decline suggests a rather general property of cortex, that its state is stabilized by an input.

    View details for DOI 10.1038/nn.2501

    View details for Web of Science ID 000274860100020

    View details for PubMedID 20173745

  • A central source of movement variability NEURON Churchland, M. M., Afshar, A., Shenoy, K. V. 2006; 52 (6): 1085-1096

    Abstract

    Movements are universally, sometimes frustratingly, variable. When such variability causes error, we typically assume that something went wrong during the movement. The same assumption is made by recent and influential models of motor control. These posit that the principal limit on repeatable performance is neuromuscular noise that corrupts movement as it occurs. An alternative hypothesis is that movement variability arises before movements begin, during motor preparation. We examined this possibility directly by recording the preparatory activity of single cortical neurons during a highly practiced reach task. Small variations in preparatory neural activity were predictive of small variations in the upcoming reach. Effect magnitudes were such that at least half of the observed movement variability likely had its source during motor preparation. Thus, even for a highly practiced task, the ability to repeatedly plan the same movement limits our ability to repeatedly execute the same movement.

    View details for DOI 10.1016/j.neuron.2006.10.034

    View details for Web of Science ID 000243115100015

    View details for PubMedID 17178410

  • A high-performance brain-computer interface NATURE Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., Shenoy, K. V. 2006; 442 (7099): 195-198

    Abstract

    Recent studies have demonstrated that monkeys and humans can use signals from the brain to guide computer cursors. Brain-computer interfaces (BCIs) may one day assist patients suffering from neurological injury or disease, but relatively low system performance remains a major obstacle. In fact, the speed and accuracy with which keys can be selected using BCIs is still far lower than for systems relying on eye movements. This is true whether BCIs use recordings from populations of individual neurons using invasive electrode techniques or electroencephalogram recordings using less- or non-invasive techniques. Here we present the design and demonstration, using electrode arrays implanted in monkey dorsal premotor cortex, of a manyfold higher performance BCI than previously reported. These results indicate that a fast and accurate key selection system, capable of operating with a range of keyboard sizes, is possible (up to 6.5 bits per second, or approximately 15 words per minute, with 96 electrodes). The highest information throughput is achieved with unprecedentedly brief neural recordings, even as recording quality degrades over time. These performance results and their implications for system design should substantially increase the clinical viability of BCIs in humans.

    View details for DOI 10.1038/nature04968

    View details for Web of Science ID 000238979700046

    View details for PubMedID 16838020

  • Neural Dynamics of Reaching following Incorrect or Absent Motor Preparation NEURON Ames, K. C., Ryu, S. I., Shenoy, K. V. 2014; 81 (2): 438-451

    Abstract

    Moving is thought to take separate preparation and execution steps. During preparation, neural activity in primary motor and dorsal premotor cortices achieves a state specific to an upcoming action but movements are not performed until the execution phase. We investigated whether this preparatory state (more precisely, prepare-and-hold state) is required for movement execution using two complementary experiments. We compared monkeys' neural activity during delayed and nondelayed reaches and in a delayed reaching task in which the target switched locations on a small percentage of trials. Neural population activity bypassed the prepare-and-hold state both in the absence of a delay and if the wrong reach was prepared. However, the initial neural response to the target was similar across behavioral conditions. This suggests that the prepare-and-hold state can be bypassed if needed, but there is a short-latency preparatory step that is performed prior to movement even without a delay.

    View details for DOI 10.1016/j.neuron.2013.12.003

    View details for Web of Science ID 000330420700020

    View details for PubMedID 24462104

  • Cortical activity in the null space: permitting preparation without movement Nature Neuroscience Kaufman, M., T. 2014
  • Intention estimation in brain-machine interfaces Journal of Neural Engineering Fan, J., M., Nuyujukian, P., Kao, J., Chestek, C. A., Ryu, S. I., Shenoy, K. V. 2014; 11:016004
  • DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity. Journal of neural engineering Cowley, B. R., Kaufman, M. T., Butler, Z. S., Churchland, M. M., Ryu, S. I., Shenoy, K. V., Yu, B. M. 2013; 10 (6): 066012-?

    Abstract

    Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

    View details for DOI 10.1088/1741-2560/10/6/066012

    View details for PubMedID 24216250

  • A coaxial optrode as multifunction write-read probe for optogenetic studies in non-human primates. Journal of neuroscience methods Ozden, I., Wang, J., Lu, Y., May, T., Lee, J., Goo, W., O'Shea, D. J., Kalanithi, P., Diester, I., Diagne, M., Deisseroth, K., Shenoy, K. V., Nurmikko, A. V. 2013; 219 (1): 142-154

    Abstract

    Advances in optogenetics have led to first reports of expression of light-gated ion-channels in non-human primates (NHPs). However, a major obstacle preventing effective application of optogenetics in NHPs and translation to optogenetic therapeutics is the absence of compatible multifunction optoelectronic probes for (1) precision light delivery, (2) low-interference electrophysiology, (3) protein fluorescence detection, and (4) repeated insertion with minimal brain trauma.Here we describe a novel brain probe device, a "coaxial optrode", designed to minimize brain tissue damage while microfabricated to perform simultaneous electrophysiology, light delivery and fluorescence measurements in the NHP brain. The device consists of a tapered, gold-coated optical fiber inserted in a polyamide tube. A portion of the gold coating is exposed at the fiber tip to allow electrophysiological recordings in addition to light delivery/collection at the tip.Coaxial optrode performance was demonstrated by experiments in rodents and NHPs, and characterized by computational models. The device mapped opsin expression in the brain and achieved precisely targeted optical stimulation and electrophysiology with minimal cortical damage.Overall, combined electrical, optical and mechanical features of the coaxial optrode allowed a performance for NHP studies which was not possible with previously existing devices.Coaxial optrode is currently being used in two NHP laboratories as a major tool to study brain function by inducing light modulated neural activity and behavior. By virtue of its design, the coaxial optrode can be extended for use as a chronic implant and multisite neural stimulation/recording.

    View details for DOI 10.1016/j.jneumeth.2013.06.011

    View details for PubMedID 23867081

  • The roles of monkey M1 neuron classes in movement preparation and execution JOURNAL OF NEUROPHYSIOLOGY Kaufman, M. T., Churchland, M. M., Shenoy, K. V. 2013; 110 (4): 817-825

    Abstract

    The motor cortices exhibit substantial activity while preparing movements, yet the arm remains still during preparation. We investigated whether a subpopulation of presumed inhibitory neurons in primary motor cortex (M1) might be involved in "gating" motor output during preparation, while permitting output during movement. This hypothesis predicts a release of inhibition just before movement onset. In data from M1 of two monkeys, we did not find evidence for this hypothesis: few neurons exhibited a clear pause during movement, and these were at the tail end of a broad distribution. We then identified a subpopulation likely to be enriched for inhibitory interneurons, using their waveform shapes. We found that the firing rates of this subpopulation tended to increase during movement instead of decreasing as predicted by the M1 gating model. No clear subset that might implement an inhibitory gate was observed. Together with previous evidence against upstream inhibitory mechanisms in premotor cortex, this provides evidence against an inhibitory "gate" for motor output in cortex. Instead, it appears that some other mechanism must likely exist.

    View details for DOI 10.1152/jn.00892.2011

    View details for Web of Science ID 000323208800003

    View details for PubMedID 23699057

  • Investigating the role of firing-rate normalization and dimensionality reduction in brain-machine interface robustness. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Kao, J. C., Nuyujukian, P., Stavisky, S., Ryu, S. I., Ganguli, S., Shenoy, K. V. 2013; 2013: 293-298

    Abstract

    The intraday robustness of brain-machine interfaces (BMIs) is important to their clinical viability. In particular, BMIs must be robust to intraday perturbations in neuron firing rates, which may arise from several factors including recording loss and external noise. Using a state-of-the-art decode algorithm, the Recalibrated Feedback Intention Trained Kalman filter (ReFIT-KF) [1] we introduce two novel modifications: (1) a normalization of the firing rates, and (2) a reduction of the dimensionality of the data via principal component analysis (PCA). We demonstrate in online studies that a ReFIT-KF equipped with normalization and PCA (NPC-ReFIT-KF) (1) achieves comparable performance to a standard ReFIT-KF when at least 60% of the neural variance is captured, and (2) is more robust to the undetected loss of channels. We present intuition as to how both modifications may increase the robustness of BMIs, and investigate the contribution of each modification to robustness. These advances, which lead to a decoder achieving state-of-the-art performance with improved robustness, are important for the clinical viability of BMI systems.

    View details for DOI 10.1109/EMBC.2013.6609495

    View details for PubMedID 24109682

  • Design and validation of a real-time spiking-neural-network decoder for brain-machine interfaces. Journal of neural engineering Dethier, J., Nuyujukian, P., Ryu, S. I., Shenoy, K. V., Boahen, K. 2013; 10 (3): 036008-?

    Abstract

    Objective. Cortically-controlled motor prostheses aim to restore functions lost to neurological disease and injury. Several proof of concept demonstrations have shown encouraging results, but barriers to clinical translation still remain. In particular, intracortical prostheses must satisfy stringent power dissipation constraints so as not to damage cortex. Approach. One possible solution is to use ultra-low power neuromorphic chips to decode neural signals for these intracortical implants. The first step is to explore in simulation the feasibility of translating decoding algorithms for brain-machine interface (BMI) applications into spiking neural networks (SNNs). Main results. Here we demonstrate the validity of the approach by implementing an existing Kalman-filter-based decoder in a simulated SNN using the Neural Engineering Framework (NEF), a general method for mapping control algorithms onto SNNs. To measure this system's robustness and generalization, we tested it online in closed-loop BMI experiments with two rhesus monkeys. Across both monkeys, a Kalman filter implemented using a 2000-neuron SNN has comparable performance to that of a Kalman filter implemented using standard floating point techniques. Significance. These results demonstrate the tractability of SNN implementations of statistical signal processing algorithms on different monkeys and for several tasks, suggesting that a SNN decoder, implemented on a neuromorphic chip, may be a feasible computational platform for low-power fully-implanted prostheses. The validation of this closed-loop decoder system and the demonstration of its robustness and generalization hold promise for SNN implementations on an ultra-low power neuromorphic chip using the NEF.

    View details for DOI 10.1088/1741-2560/10/3/036008

    View details for PubMedID 23574919

  • Hand posture classification using electrocorticography signals in the gamma band over human sensorimotor brain areas JOURNAL OF NEURAL ENGINEERING Chestek, C. A., Gilja, V., Blabe, C. H., Foster, B. L., Shenoy, K. V., Parvizi, J., Henderson, J. M. 2013; 10 (2)

    Abstract

    Brain-machine interface systems translate recorded neural signals into command signals for assistive technology. In individuals with upper limb amputation or cervical spinal cord injury, the restoration of a useful hand grasp could significantly improve daily function. We sought to determine if electrocorticographic (ECoG) signals contain sufficient information to select among multiple hand postures for a prosthetic hand, orthotic, or functional electrical stimulation system.We recorded ECoG signals from subdural macro- and microelectrodes implanted in motor areas of three participants who were undergoing inpatient monitoring for diagnosis and treatment of intractable epilepsy. Participants performed five distinct isometric hand postures, as well as four distinct finger movements. Several control experiments were attempted in order to remove sensory information from the classification results. Online experiments were performed with two participants.Classification rates were 68%, 84% and 81% for correct identification of 5 isometric hand postures offline. Using 3 potential controls for removing sensory signals, error rates were approximately doubled on average (2.1×). A similar increase in errors (2.6×) was noted when the participant was asked to make simultaneous wrist movements along with the hand postures. In online experiments, fist versus rest was successfully classified on 97% of trials; the classification output drove a prosthetic hand. Online classification performance for a larger number of hand postures remained above chance, but substantially below offline performance. In addition, the long integration windows used would preclude the use of decoded signals for control of a BCI system.These results suggest that ECoG is a plausible source of command signals for prosthetic grasp selection. Overall, avenues remain for improvement through better electrode designs and placement, better participant training, and characterization of non-stationarities such that ECoG could be a viable signal source for grasp control for amputees or individuals with paralysis.

    View details for DOI 10.1088/1741-2560/10/2/026002

    View details for Web of Science ID 000316728700003

    View details for PubMedID 23369953

  • A recurrent neural network that produces EMG from rhythmic dynamics. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Sussillo, D., Churchland, M. M., Kaufman, M. T., Shenoy, K. V. 2013: III-67
  • Quantifying representational and dynamical structure in large neural datasets. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Seely, J., Kaufman, M. T., Kohn, A., Smith, M., Movshon, A., Priebe, N., Shenoy, Krishna, V. 2013
  • Selective integration of sensory evidence by recurrent dynamics in prefrontal cortex. Nature. Mante, V., Sussillo, D., Shenoy, K. V., Newsome, W. T. 2013; 503: 78-8, 45-47
  • Characterization of dynamical activity in motor cortex. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Elsayed, G., Kaufman, M. T., Ryu, S. I., Shenoy, K. V., Churchland, M. M., Cunningham, J. P. 2013
  • Neural dynamics following optogenetic disruption of motor preparation. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) O'Shea, D., Goo, W., Kalanithi, P., Diester, I., Ramakrishnan, C., Deisseroth, K., Shenoy, Krishna, V. 2013
  • High performance computer cursor control using neuronal ensemble recordings from the motor cortex of a person with ALS. Neurosurgery. Henderson, J. M., Gilja, V., Pandarinath, C., Blabe, C., Hochberg, L. R., Shenoy, K. V. 2013; 1:184: 60
  • DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity. Journal of Neural Engineering. Cowley, B. R., Kaufman, M. T., Butler, Z. S., Churchland, M. M., Ryu, S. I., Shenoy, K. V. 2013; 10:066012
  • Dimensionality, dynamics, and correlations in the motor cortical substrate for reaching. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Gao, P., rautmann, E., Yu, B. M., Santhanam, G., Ryu, S. I., Shenoy, K. V. 2013
  • An L (1)-regularized logistic model for detecting short-term neuronal interactions JOURNAL OF COMPUTATIONAL NEUROSCIENCE Zhao, M., Batista, A., Cunningham, J. P., Chestek, C., Rivera-Alvidrez, Z., Kalmar, R., Ryu, S., Shenoy, K., Iyengar, S. 2012; 32 (3): 479-497

    Abstract

    Interactions among neurons are a key component of neural signal processing. Rich neural data sets potentially containing evidence of interactions can now be collected readily in the laboratory, but existing analysis methods are often not sufficiently sensitive and specific to reveal these interactions. Generalized linear models offer a platform for analyzing multi-electrode recordings of neuronal spike train data. Here we suggest an L(1)-regularized logistic regression model (L(1)L method) to detect short-term (order of 3 ms) neuronal interactions. We estimate the parameters in this model using a coordinate descent algorithm, and determine the optimal tuning parameter using a Bayesian Information Criterion. Simulation studies show that in general the L(1)L method has better sensitivities and specificities than those of the traditional shuffle-corrected cross-correlogram (covariogram) method. The L(1)L method is able to detect excitatory interactions with both high sensitivity and specificity with reasonably large recordings, even when the magnitude of the interactions is small; similar results hold for inhibition given sufficiently high baseline firing rates. Our study also suggests that the false positives can be further removed by thresholding, because their magnitudes are typically smaller than true interactions. Simulations also show that the L(1)L method is somewhat robust to partially observed networks. We apply the method to multi-electrode recordings collected in the monkey dorsal premotor cortex (PMd) while the animal prepares to make reaching arm movements. The results show that some neurons interact differently depending on task conditions. The stronger interactions detected with our L(1)L method were also visible using the covariogram method.

    View details for DOI 10.1007/s10827-011-0365-5

    View details for Web of Science ID 000303589500007

    View details for PubMedID 22038503

  • A recurrent neural network for closed-loop intracortical brain-machine interface decoders JOURNAL OF NEURAL ENGINEERING Sussillo, D., Nuyujukian, P., Fan, J. M., Kao, J. C., Stavisky, S. D., Ryu, S., Shenoy, K. 2012; 9 (2)

    Abstract

    Recurrent neural networks (RNNs) are useful tools for learning nonlinear relationships in time series data with complex temporal dependences. In this paper, we explore the ability of a simplified type of RNN, one with limited modifications to the internal weights called an echostate network (ESN), to effectively and continuously decode monkey reaches during a standard center-out reach task using a cortical brain-machine interface (BMI) in a closed loop. We demonstrate that the RNN, an ESN implementation termed a FORCE decoder (from first order reduced and controlled error learning), learns the task quickly and significantly outperforms the current state-of-the-art method, the velocity Kalman filter (VKF), using the measure of target acquire time. We also demonstrate that the FORCE decoder generalizes to a more difficult task by successfully operating the BMI in a randomized point-to-point task. The FORCE decoder is also robust as measured by the success rate over extended sessions. Finally, we show that decoded cursor dynamics are more like naturalistic hand movements than those of the VKF. Taken together, these results suggest that RNNs in general, and the FORCE decoder in particular, are powerful tools for BMI decoder applications.

    View details for DOI 10.1088/1741-2560/9/2/026027

    View details for Web of Science ID 000302144100027

    View details for PubMedID 22427488

  • HermesE: A 96-Channel Full Data Rate Direct Neural Interface in 0.13 mu m CMOS IEEE JOURNAL OF SOLID-STATE CIRCUITS Gao, H., Walker, R. M., Nuyujukian, P., Makinwa, K. A., Shenoy, K. V., Murmann, B., Meng, T. H. 2012; 47 (4): 1043-1055
  • Brain Enabled by Next-Generation Neurotechnology: Using Multiscale and Multimodal Models IEEE PULSE Shenoy, K. V., Nurmikko, A. V. 2012; 3 (2): 31-36

    Abstract

    As many articles in this issue of IEEE Pulse demonstrate, interfacing directly with the brain presents several fundamental challenges. These challenges reside at multiple levels and span many disciplines, ranging from the need to understand brain states at the level of neural circuits to creating technological innovations to facilitate new therapeutic options. The goal of our multiuniversity research team, composed of researchers from Stanford University, Brown University, the University of California at San Francisco (UCSF), and the University College London (UCL), is to substantially elevate the fundamental understanding of brain information processing and its relationship with sensation, behavior, and injury. Our team was assembled to provide expertise ranging from neuroscience to neuroengineering and to neurological and psychiatric clinical guidance, all of which are critical to the overarching research goal. By employing a suite of innovative experimental, computational, and theoretical approaches, the Defense Advanced Research Projects Agency (DARPA) Reorganization and Plasticity to Accelerate Injury Recovery (REPAIR) team has set its sights on learning how the brain and its microcircuitry react (e.g., to sudden physiological changes) and what can be done to encourage recovery from such (reversible) injury. In this article, we summarize some of the team's technical goals, approaches, and early illustrative results.

    View details for DOI 10.1109/MPUL.2011.2181021

    View details for Web of Science ID 000307806600009

    View details for PubMedID 22481743

  • A brain machine interface control algorithm designed from a feedback control perspective 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Fan, J. M., Ryu, S. I., Shenoy, K. V. 2012: 1318-1322

    Abstract

    We present a novel brain machine interface (BMI) control algorithm, the recalibrated feedback intention-trained Kalman filter (ReFIT-KF). The design of ReFIT-KF is motivated from a feedback control perspective applied to existing BMI control algorithms. The result is two design innovations that alter the modeling assumptions made by these algorithms and the methods by which these algorithms are trained. In online neural control experiments recording from a 96-electrode array implanted in M1 of a macaque monkey, the ReFIT-KF control algorithm demonstrates large performance improvements over the current state of the art velocity Kalman filter, reducing target acquisition time by a factor of two, while maintaining a 500 ms hold period, thereby increasing the clinical viability of BMI systems.

    View details for Web of Science ID 000313296501144

    View details for PubMedID 23366141

  • Concurrent integration and gating of sensory information with orthogonal mixed representations. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Mante, V., Sussillo, D., Shenoy, K. V., Newsome, W. T. 2012: II-58
  • Long-term decoding stability without retraining for intracortical brain computer Interface. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Bishop, W., Nuyujukian, P., Chestek, C. A., Gilja, V., Ryu, S. I., Shenoy, K. V. 2012: III-40
  • Dimensionality in motor cortex: differences between models and experiment. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Seely, J., Kaufman, M. T., Ryu, S. I., Cunningham, J. P., Shenoy, K. V., Churchland, M. M. 2012: II-67
  • 2010 DARPA neural engineering, science, and technology forum [Guest Editorial]. IEEE EMBS. Schnitzer, J. J. 2012; 0.131944444444444
  • Brain models enabled by next-generation neurotechnology. Pulse Magazine, IEEE Engineering in Medicine and Biology Society. Shenoy, K. V., Nurmikko, A. V. 2012; 3: 31-36.
  • Neural dynamics of reaching following incomplete or incorrect planning. Frontiers in Neuroscience. Ames, K. C., Ryu, S. I., Shenoy, K. V. 2012: T-5.
  • A recurrent neural network that produces EMG from thythmic dynamics. Translational and Computational Motor Control (TCMC) pre-meeting to Society for Neuroscience annual meeting, New Orleans, LA. Sussillo, D., Churchland, M. M., Kaufman, M. T., Shenoy, K. V. 2012
  • Identifying the neural initiation of a movement. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Petreska, B., Kaufman, M. T., Churchland, M. M., Ryu, S. I., Shenoy, K. V., Sahani, M. 2012: I-66
  • A high-performance, robust brain-machine interface without retraining. Frontiers in Neuroscience. Nuyujukian, P., Kao, J., Fan, J. M., Stavisky, S., Ryu, S. I., Shenoy, K. V. 2012: III-65
  • A framework for relating neural activity to freely moving behavior 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) Foster, J. D., Nuyujukian, P., Freifeld, O., Ryu, S. I., Black, M. J., Shenoy, K. V. 2012: 2736-2739

    Abstract

    Two research communities, motor systems neuroscience and motor prosthetics, examine the relationship between neural activity in the motor cortex and movement. The former community aims to understand how the brain controls and generates movement; the latter community focuses on how to decode neural activity as control signals for a prosthetic cursor or limb. Both have made progress toward understanding the relationship between neural activity in the motor cortex and behavior. However, these findings are tested using animal models in an environment that constrains behavior to simple, limited movements. These experiments show that, in constrained settings, simple reaching motions can be decoded from small populations of spiking neurons. It is unclear whether these findings hold for more complex, full-body behaviors in unconstrained settings. Here we present the results of freely-moving behavioral experiments from a monkey with simultaneous intracortical recording. We investigated neural firing rates while the monkey performed various tasks such as walking on a treadmill, reaching for food, and sitting idly. We show that even in such an unconstrained and varied context, neural firing rates are well tuned to behavior, supporting findings of basic neuroscience. Further, we demonstrate that the various behavioral tasks can be reliably classified with over 95% accuracy, illustrating the viability of decoding techniques despite significant variation and environmental distractions associated with unconstrained behavior. Such encouraging results hint at potential utility of the freely-moving experimental paradigm.

    View details for Web of Science ID 000313296502238

    View details for PubMedID 23366491

  • DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity 2012 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) Cowley, B. R., Kaufman, M. T., Churchland, M. M., Ryu, S. I., Shenoy, K. V., Yu, B. M. 2012: 4607-4610

    Abstract

    The activity of tens to hundreds of neurons can be succinctly summarized by a smaller number of latent variables extracted using dimensionality reduction methods. These latent variables define a reduced-dimensional space in which we can study how population activity varies over time, across trials, and across experimental conditions. Ideally, we would like to visualize the population activity directly in the reduced-dimensional space, whose optimal dimensionality (as determined from the data) is typically greater than 3. However, direct plotting can only provide a 2D or 3D view. To address this limitation, we developed a Matlab graphical user interface (GUI) that allows the user to quickly navigate through a continuum of different 2D projections of the reduced-dimensional space. To demonstrate the utility and versatility of this GUI, we applied it to visualize population activity recorded in premotor and motor cortices during reaching tasks. Examples include single-trial population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded sequentially using single electrodes. Because any single 2D projection may provide a misleading impression of the data, being able to see a large number of 2D projections is critical for intuition-and hypothesis-building during exploratory data analysis. The GUI includes a suite of additional interactive tools, including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses. The use of visualization tools like the GUI developed here, in tandem with dimensionality reduction methods, has the potential to further our understanding of neural population activity.

    View details for Web of Science ID 000313296504203

    View details for PubMedID 23366954

  • Challenges and Opportunities for Next-Generation Intracortically Based Neural Prostheses IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Gilja, V., Chestek, C. A., Diester, I., Henderson, J. M., Deisseroth, K., Shenoy, K. V. 2011; 58 (7): 1891-1899

    Abstract

    Neural prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding computer cursors, prosthetic arms, and other assistive devices. Intracortical electrode arrays measure action potentials and local field potentials from individual neurons, or small populations of neurons, in the motor cortices and can provide considerable information for controlling prostheses. Despite several compelling proof-of-concept laboratory animal experiments and an initial human clinical trial, at least three key challenges remain which, if left unaddressed, may hamper the translation of these systems into widespread clinical use. We review these challenges: achieving able-bodied levels of performance across tasks and across environments, achieving robustness across multiple decades, and restoring able-bodied quality proprioception and somatosensation. We also describe some emerging opportunities for meeting these challenges. If these challenges can be largely or fully met, intracortically based neural prostheses may achieve true clinical viability and help increasing numbers of disabled patients.

    View details for DOI 10.1109/TBME.2011.2107553

    View details for Web of Science ID 000291890000003

    View details for PubMedID 21257365

  • A closed-loop human simulator for investigating the role of feedback control in brain-machine interfaces JOURNAL OF NEUROPHYSIOLOGY Cunningham, J. P., Nuyujukian, P., Gilja, V., Chestek, C. A., Ryu, S. I., Shenoy, K. V. 2011; 105 (4): 1932-1949

    Abstract

    Neural prosthetic systems seek to improve the lives of severely disabled people by decoding neural activity into useful behavioral commands. These systems and their decoding algorithms are typically developed "offline," using neural activity previously gathered from a healthy animal, and the decoded movement is then compared with the true movement that accompanied the recorded neural activity. However, this offline design and testing may neglect important features of a real prosthesis, most notably the critical role of feedback control, which enables the user to adjust neural activity while using the prosthesis. We hypothesize that understanding and optimally designing high-performance decoders require an experimental platform where humans are in closed-loop with the various candidate decode systems and algorithms. It remains unexplored the extent to which the subject can, for a particular decode system, algorithm, or parameter, engage feedback and other strategies to improve decode performance. Closed-loop testing may suggest different choices than offline analyses. Here we ask if a healthy human subject, using a closed-loop neural prosthesis driven by synthetic neural activity, can inform system design. We use this online prosthesis simulator (OPS) to optimize "online" decode performance based on a key parameter of a current state-of-the-art decode algorithm, the bin width of a Kalman filter. First, we show that offline and online analyses indeed suggest different parameter choices. Previous literature and our offline analyses agree that neural activity should be analyzed in bins of 100- to 300-ms width. OPS analysis, which incorporates feedback control, suggests that much shorter bin widths (25-50 ms) yield higher decode performance. Second, we confirm this surprising finding using a closed-loop rhesus monkey prosthetic system. These findings illustrate the type of discovery made possible by the OPS, and so we hypothesize that this novel testing approach will help in the design of prosthetic systems that will translate well to human patients.

    View details for DOI 10.1152/jn.00503.2010

    View details for Web of Science ID 000289620500044

    View details for PubMedID 20943945

  • Adaptive Resolution ADC Array for an Implantable Neural Sensor IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS O'Driscoll, S., Shenoy, K. V., Meng, T. H. 2011; 5 (2): 120-130
  • Spiking Neural Network Decoder for Brain-Machine Interfaces 2011 5TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) Dethier, J., Gilja, V., Nuyujukian, P., Elassaad, S. A., Shenoy, K. V., Boahen, K. 2011: 396-399
  • A dynamical systems view of motor preparation: Implications for neural prosthetic system design ENHANCING PERFORMANCE FOR ACTION AND PERCEPTION: MULTISENSORY INTEGRATION, NEUROPLASTICITY AND NEUROPROSTHETICS, PT II Shenoy, K. V., Kaufman, M. T., Sahani, M., Churchland, M. M. 2011; 192: 33-58

    Abstract

    Neural prosthetic systems aim to help disabled patients suffering from a range of neurological injuries and disease by using neural activity from the brain to directly control assistive devices. This approach in effect bypasses the dysfunctional neural circuitry, such as an injured spinal cord. To do so, neural prostheses depend critically on a scientific understanding of the neural activity that drives them. We review here several recent studies aimed at understanding the neural processes in premotor cortex that precede arm movements and lead to the initiation of movement. These studies were motivated by hypotheses and predictions conceived of within a dynamical systems perspective. This perspective concentrates on describing the neural state using as few degrees of freedom as possible and on inferring the rules that govern the motion of that neural state. Although quite general, this perspective has led to a number of specific predictions that have been addressed experimentally. It is hoped that the resulting picture of the dynamical role of preparatory and movement-related neural activity will be particularly helpful to the development of neural prostheses, which can themselves be viewed as dynamical systems under the control of the larger dynamical system to which they are attached.

    View details for DOI 10.1016/B978-0-444-53355-5.00003-8

    View details for Web of Science ID 000310992900004

    View details for PubMedID 21763517

  • Extracting rotational structure from motor cortical data. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Cunningham, J. P., Churchland, M. M., Kaufman, M. T., Shenoy, K. V. 2011: II-33.
  • The role of horizontal long-range connections in shaping the dynamics of multi-electrode array data. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Lerchner, A., Shenoy, K. V., Sahani, M. 2011: I-27.
  • Firing rate oscillations underlie motor cortex responses during reaching in monkey. Frontiers in Neuroscience. Churchland, M. M., Cunningham, J. P., Kaufman, M. T., Ryu, S., Shenoy, K. V. 2011: III-32.
  • Previews: New insights into motor cortex. Neuron. Graziano, M. S. 2011; 71: 387-388
  • Modelling low-dimensional dynamics in recorded spiking populations. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Macke, J., Busing, L., Cunningham, J. P., Yu, B. M., Shenoy, K. V., Sahani, M. 2011: I-34.
  • Detecting changes in neural dynamics within single trials. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Petreska, B., Cunningham, J. P., Santhanam, G., Yu, B. M., Ryu, S. I., Shenoy, K. V. 2011: I-33.
  • Cortical preparatory activity avoids causing movement by remaining in a muscle-neutral space. Frontiers in Neuroscience.Conference Abstract: Computational and Systems Neuroscience (COSYNE) Kaufman, M. T., Churchland, M. M., Shenoy, K. V. 2011: II-61.
  • Combining Wireless Neural Recording and Video Capture for the Analysis of Natural Gait 2011 5TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER) Foster, J. D., Freifeld, O., Nuyujukian, P., Ryu, S. I., Black, M. J., Shenoy, K. V. 2011: 613-616
  • Autonomous head-mounted electrophysiology systems for freely behaving primates CURRENT OPINION IN NEUROBIOLOGY Gilja, V., Chestek, C. A., Nuyujukian, P., Foster, J., Shenoy, K. V. 2010; 20 (5): 676-686

    Abstract

    Recent technological advances have led to new light-weight battery-operated systems for electrophysiology. Such systems are head mounted, run for days without experimenter intervention, and can record and stimulate from single or multiple electrodes implanted in a freely behaving primate. Here we discuss existing systems, studies that use them, and how they can augment traditional, physically restrained, 'in-rig' electrophysiology. With existing technical capabilities, these systems can acquire multiple signal classes, such as spikes, local field potential, and electromyography signals, and can stimulate based on real-time processing of recorded signals. Moving forward, this class of technologies, along with advances in neural signal processing and behavioral monitoring, have the potential to dramatically expand the scope and scale of electrophysiological studies.

    View details for DOI 10.1016/j.conb.2010.06.007

    View details for Web of Science ID 000283481100022

    View details for PubMedID 20655733

  • Roles of Monkey Premotor Neuron Classes in Movement Preparation and Execution JOURNAL OF NEUROPHYSIOLOGY Kaufman, M. T., Churchland, M. M., Santhanam, G., Yu, B. M., Afshar, A., Ryu, S. I., Shenoy, K. V. 2010; 104 (2): 799-810

    Abstract

    Dorsal premotor cortex (PMd) is known to be involved in the planning and execution of reaching movements. However, it is not understood how PMd plan activity-often present in the very same neurons that respond during movement-is prevented from itself producing movement. We investigated whether inhibitory interneurons might "gate" output from PMd, by maintaining high levels of inhibition during planning and reducing inhibition during execution. Recently developed methods permit distinguishing interneurons from pyramidal neurons using extracellular recordings. We extend these methods here for use with chronically implanted multi-electrode arrays. We then applied these methods to single- and multi-electrode recordings in PMd of two monkeys performing delayed-reach tasks. Responses of putative interneurons were not generally in agreement with the hypothesis that they act to gate output from the area: in particular it was not the case that interneurons tended to reduce their firing rates around the time of movement. In fact, interneurons increased their rates more than putative pyramidal neurons during both the planning and movement epochs. The two classes of neurons also differed in a number of other ways, including greater modulation across conditions for interneurons, and interneurons more frequently exhibiting increases in firing rate during movement planning and execution. These findings provide novel information about the greater responsiveness of putative PMd interneurons in motor planning and execution and suggest that we may need to consider new possibilities for how planning activity is structured such that it does not itself produce movement.

    View details for DOI 10.1152/jn.00231.2009

    View details for Web of Science ID 000280932400023

    View details for PubMedID 20538784

  • HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS Miranda, H., Gilja, V., Chestek, C. A., Shenoy, K. V., Meng, T. H. 2010; 4 (3): 181-191
  • Preparatory tuning in premotor cortex relates most closely to the pop- ulation movement-epoch response. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE) Churchland, M. M., Kaufman, M. T., Cunningham, J. P., Shenoy, K. V. 2010
  • The roles of monkey premotor neuron classes in movement preparation and execution. Journal of Neurophysiology. Kaufman, M. T., Churchland, M. M., Santhanam, G., Yu, B. M., Afshar, A., Ryu, S. I., Shenoy, Krishna, V. 2010; 104: 799-810.
  • Ensemble activity underlying movement preparation in prearcuate cortex. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE) Kalmar, R., Reppas, J., Ryu, S. I., Shenoy, K. V., Newsome, W. T. 2010
  • High-performance continuous neural cursor control enabled by a feedback control perspective. Frontiers in Neuroscience. Frontiers in Neuroscience. Conference Abstract: Computational and Systems Neuroscience (COSYNE), Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Ryu, S. I., Shenoy, Krishna, V. 2010
  • Toward human cortical prostheses: Addressing the performance barrier to clinical reality. Abstract #164. Congress of Neurological Surgeons Annual Meeting Abstracts Ryu, S. I., Gilja, V., Nuyujukian, P., Chestek, C. A., Yu, B. M., Shenoy, K. V. 2010
  • Editorial overview -- Special section on New Technologies. Current Opinion in Neurobiology. Schuman, E., Zhuang, X. 2010; 20: 608-609.
  • Motor systems CURRENT OPINION IN NEUROBIOLOGY El Manira, A., Shenoy, K. 2009; 19 (6): 570-571

    View details for DOI 10.1016/j.conb.2009.10.015

    View details for Web of Science ID 000273864300002

    View details for PubMedID 19897357

  • Methods for estimating neural firing rates, and their application to brain-machine interfaces NEURAL NETWORKS Cunningham, J. P., Gilja, V., Ryu, S. I., Shenoy, K. V. 2009; 22 (9): 1235-1246

    Abstract

    Neural spike trains present analytical challenges due to their noisy, spiking nature. Many studies of neuroscientific and neural prosthetic importance rely on a smoothed, denoised estimate of a spike train's underlying firing rate. Numerous methods for estimating neural firing rates have been developed in recent years, but to date no systematic comparison has been made between them. In this study, we review both classic and current firing rate estimation techniques. We compare the advantages and drawbacks of these methods. Then, in an effort to understand their relevance to the field of neural prostheses, we also apply these estimators to experimentally gathered neural data from a prosthetic arm-reaching paradigm. Using these estimates of firing rate, we apply standard prosthetic decoding algorithms to compare the performance of the different firing rate estimators, and, perhaps surprisingly, we find minimal differences. This study serves as a review of available spike train smoothers and a first quantitative comparison of their performance for brain-machine interfaces.

    View details for DOI 10.1016/j.neunet.2009.02.004

    View details for Web of Science ID 000272073800005

    View details for PubMedID 19349143

  • Factor-Analysis Methods for Higher-Performance Neural Prostheses JOURNAL OF NEUROPHYSIOLOGY Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani, M., Shenoy, K. V. 2009; 102 (2): 1315-1330

    Abstract

    Neural prostheses aim to provide treatment options for individuals with nervous-system disease or injury. It is necessary, however, to increase the performance of such systems before they can be clinically viable for patients with motor dysfunction. One performance limitation is the presence of correlated trial-to-trial variability that can cause neural responses to wax and wane in concert as the subject is, for example, more attentive or more fatigued. If a system does not properly account for this variability, it may mistakenly interpret such variability as an entirely different intention by the subject. We report here the design and characterization of factor-analysis (FA)-based decoding algorithms that can contend with this confound. We characterize the decoders (classifiers) on experimental data where monkeys performed both a real reach task and a prosthetic cursor task while we recorded from 96 electrodes implanted in dorsal premotor cortex. The decoder attempts to infer the underlying factors that comodulate the neurons' responses and can use this information to substantially lower error rates (one of eight reach endpoint predictions) by 150 ms, although still advantageous at shorter timescales, that Gaussian-based algorithms performed better than the analogous Poisson-based algorithms and that the FA algorithm is robust even with a limited amount of training data. We propose that FA-based methods are effective in modeling correlated trial-to-trial neural variability and can be used to substantially increase overall prosthetic system performance.

    View details for DOI 10.1152/jn.00097.2009

    View details for Web of Science ID 000269500400061

    View details for PubMedID 19297518

  • Wireless Neural Recording With Single Low-Power Integrated Circuit IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Harrison, R. R., Kier, R. J., Chestek, C. A., Gilja, V., Nuyujukian, P., Ryu, S., Greger, B., Solzbacher, F., Shenoy, K. V. 2009; 17 (4): 322-329

    Abstract

    We present benchtop and in vivo experimental results from an integrated circuit designed for wireless implantable neural recording applications. The chip, which was fabricated in a commercially available 0.6- mum 2P3M BiCMOS process, contains 100 amplifiers, a 10-bit analog-to-digital converter (ADC), 100 threshold-based spike detectors, and a 902-928 MHz frequency-shift-keying (FSK) transmitter. Neural signals from a selected amplifier are sampled by the ADC at 15.7 kSps and telemetered over the FSK wireless data link. Power, clock, and command signals are sent to the chip wirelessly over a 2.765-MHz inductive (coil-to-coil) link. The chip is capable of operating with only two off-chip components: a power/command receiving coil and a 100-nF capacitor.

    View details for DOI 10.1109/TNSRE.2009.2023298

    View details for Web of Science ID 000268900300003

    View details for PubMedID 19497825

  • HermesC: Low-Power Wireless Neural Recording System for Freely Moving Primates IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Chestek, C. A., Gilja, V., Nuyujukian, P., Kier, R. J., Solzbacher, F., Ryu, S. I., Harrison, R. R., Shenoy, K. V. 2009; 17 (4): 330-338

    Abstract

    Neural prosthetic systems have the potential to restore lost functionality to amputees or patients suffering from neurological injury or disease. Current systems have primarily been designed for immobile patients, such as tetraplegics functioning in a rather static, carefully tailored environment. However, an active patient such as amputee in a normal dynamic, everyday environment may be quite different in terms of the neural control of movement. In order to study motor control in a more unconstrained natural setting, we seek to develop an animal model of freely moving humans. Therefore, we have developed and tested HermesC-INI3, a system for recording and wirelessly transmitting neural data from electrode arrays implanted in rhesus macaques who are freely moving. This system is based on the integrated neural interface (INI3) microchip which amplifies, digitizes, and transmits neural data across a approximately 900 MHz wireless channel. The wireless transmission has a range of approximately 4 m in free space. All together this device consumes 15.8 mA and 63.2 mW. On a single 2 A-hr battery pack, this device runs contiguously for approximately six days. The smaller size and power consumption of the custom IC allows for a smaller package (51 x 38 x 38 mm (3)) than previous primate systems. The HermesC-INI3 system was used to record and telemeter one channel of broadband neural data at 15.7 kSps from a monkey performing routine daily activities in the home cage.

    View details for DOI 10.1109/TNSRE.2009.2023293

    View details for Web of Science ID 000268900300004

    View details for PubMedID 19497829

  • Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity JOURNAL OF NEUROPHYSIOLOGY Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V., Sahani, M. 2009; 102 (1): 614-635

    Abstract

    We consider the problem of extracting smooth, low-dimensional neural trajectories that summarize the activity recorded simultaneously from many neurons on individual experimental trials. Beyond the benefit of visualizing the high-dimensional, noisy spiking activity in a compact form, such trajectories can offer insight into the dynamics of the neural circuitry underlying the recorded activity. Current methods for extracting neural trajectories involve a two-stage process: the spike trains are first smoothed over time, then a static dimensionality-reduction technique is applied. We first describe extensions of the two-stage methods that allow the degree of smoothing to be chosen in a principled way and that account for spiking variability, which may vary both across neurons and across time. We then present a novel method for extracting neural trajectories-Gaussian-process factor analysis (GPFA)-which unifies the smoothing and dimensionality-reduction operations in a common probabilistic framework. We applied these methods to the activity of 61 neurons recorded simultaneously in macaque premotor and motor cortices during reach planning and execution. By adopting a goodness-of-fit metric that measures how well the activity of each neuron can be predicted by all other recorded neurons, we found that the proposed extensions improved the predictive ability of the two-stage methods. The predictive ability was further improved by going to GPFA. From the extracted trajectories, we directly observed a convergence in neural state during motor planning, an effect that was shown indirectly by previous studies. We then show how such methods can be a powerful tool for relating the spiking activity across a neural population to the subject's behavior on a single-trial basis. Finally, to assess how well the proposed methods characterize neural population activity when the underlying time course is known, we performed simulations that revealed that GPFA performed tens of percent better than the best two-stage method.

    View details for DOI 10.1152/jn.90941.2008

    View details for Web of Science ID 000267446000056

    View details for PubMedID 19357332

  • Human cortical prostheses: lost in translation? NEUROSURGICAL FOCUS Ryu, S. I., Shenoy, K. V. 2009; 27 (1)

    Abstract

    Direct brain control of a prosthetic system is the subject of much popular and scientific news. Neural technology and science have advanced to the point that proof-of-concept systems exist for cortically-controlled prostheses in rats, monkeys, and even humans. However, realizing the dream of making such technology available to everyone is still far off. Fortunately today there is great public and scientific interest in making this happen, but it will only occur when the functional benefits of such systems outweigh the risks. In this article, the authors briefly summarize the state of the art and then highlight many issues that will directly limit clinical translation, including system durability, system performance, and patient risk. Despite the challenges, scientists and clinicians are in the desirable position of having both public and fiscal support to begin addressing these issues directly. The ultimate challenge now is to determine definitively whether these prosthetic systems will become clinical reality or forever unrealized.

    View details for DOI 10.3171/2009.4.FOCUS0987

    View details for Web of Science ID 000268583300005

    View details for PubMedID 19569893

  • A high-rate long-range wireless transmission system for multichannel neural recording applications ISCAS: 2009 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-5 Miranda, H., Gilja, V., Chestek, C., Shenoy, K. V., Meng, T. H. 2009: 1265-1268
  • Guest Editorial -- Special section on wireless neural interfaces. IEEE TNSRE. Judy, J. W., Markovic, D. 2009; 17: 309-311.
  • Guest Editorial -- Special section on wireless neural interfaces. IEEE TNSRE Judy, J. W., Markovic, D. 2009; 17: 309-311.
  • Stimulus onset quenches neural variability:a widespread cortical phenomenon. Frontiers in Systems Neuroscience. Conference Abstract: Computational and systems neuroscience. Churchland, M. M., Yu, B. M., Cunningham, J. C., Sugrue, L., Cohen, M., Corrado, G., Shenoy, Krishna, V. 2009
  • Gaussian-process factor analysis for low-d single-trial analysis of neural population activity. Frontiers in Systems Neuroscience. Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S., Shenoy, K., Sahani, M. 2009
  • Toward Optimal Target Placement for Neural Prosthetic Devices JOURNAL OF NEUROPHYSIOLOGY Cunningham, J. P., Yu, B. M., Gilja, V., Ryu, S. I., Shenoy, K. V. 2008; 100 (6): 3445-3457

    Abstract

    Neural prosthetic systems have been designed to estimate continuous reach trajectories (motor prostheses) and to predict discrete reach targets (communication prostheses). In the latter case, reach targets are typically decoded from neural spiking activity during an instructed delay period before the reach begins. Such systems use targets placed in radially symmetric geometries independent of the tuning properties of the neurons available. Here we seek to automate the target placement process and increase decode accuracy in communication prostheses by selecting target locations based on the neural population at hand. Motor prostheses that incorporate intended target information could also benefit from this consideration. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. In simulated neural spiking data fit from two monkeys, the optimal target placement algorithm yielded statistically significant improvements up to 8 and 9% for two and sixteen targets, respectively. For four and eight targets, gains were more modest, as the target layouts found by the algorithm closely resembled the canonical layouts. We trained a monkey in this paradigm and tested the algorithm with experimental neural data to confirm some of the results found in simulation. In all, the algorithm can serve not only to create new target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. The optimal target placement algorithm developed here is the first algorithm of its kind, and it should both improve decode accuracy and help automate target placement for neural prostheses.

    View details for DOI 10.1152/jn.90833.2008

    View details for Web of Science ID 000261449400040

    View details for PubMedID 18829845

  • Detecting neural-state transitions using hidden Markov models for motor cortical prostheses JOURNAL OF NEUROPHYSIOLOGY Kemere, C., Santhanam, G., Yu, B. M., Afshar, A., Ryu, S. I., Meng, T. H., Shenoy, K. V. 2008; 100 (4): 2441-2452

    Abstract

    Neural prosthetic interfaces use neural activity related to the planning and perimovement epochs of arm reaching to afford brain-directed control of external devices. Previous research has primarily centered on accurately decoding movement intention from either plan or perimovement activity, but has assumed that temporal boundaries between these epochs are known to the decoding system. In this work, we develop a technique to automatically differentiate between baseline, plan, and perimovement epochs of neural activity. Specifically, we use a generative model of neural activity to capture how neural activity varies between these three epochs. Our approach is based on a hidden Markov model (HMM), in which the latent variable (state) corresponds to the epoch of neural activity, coupled with a state-dependent Poisson firing model. Using an HMM, we demonstrate that the time of transition from baseline to plan epochs, a transition in neural activity that is not accompanied by any external behavior changes, can be detected using a threshold on the a posteriori HMM state probabilities. Following detection of the plan epoch, we show that the intended target of a center-out movement can be detected about as accurately as that by a maximum-likelihood estimator using a window of known plan activity. In addition, we demonstrate that our HMM can detect transitions in neural activity corresponding to targets not found in training data. Thus the HMM technique for automatically detecting transitions between epochs of neural activity enables prosthetic interfaces that can operate autonomously.

    View details for DOI 10.1152/jn.00924.2007

    View details for Web of Science ID 000259967000063

    View details for PubMedID 18614757

  • Cortical neural prosthesis performance improves when eye position is monitored IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Batista, A. P., Yu, B. M., Santhanam, G., Ryu, S. I., Afshar, A., Shenoy, K. V. 2008; 16 (1): 24-31

    Abstract

    Neural prostheses that extract signals directly from cortical neurons have recently become feasible as assistive technologies for tetraplegic individuals. Significant effort toward improving the performance of these systems is now warranted. A simple technique that can improve prosthesis performance is to account for the direction of gaze in the operation of the prosthesis. This proposal stems from recent discoveries that the direction of gaze influences neural activity in several areas that are commonly targeted for electrode implantation in neural prosthetics. Here, we first demonstrate that neural prosthesis performance does improve when eye position is taken into account. We then show that eye position can be estimated directly from neural activity, and thus performance gains can be realized even without a device that tracks eye position.

    View details for DOI 10.1109/TNSRE.2007.906958

    View details for Web of Science ID 000253442400004

    View details for PubMedID 18303802

  • HermesC: RF wireless low-power neural recording system for freely behaving primates PROCEEDINGS OF 2008 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-10 Chestek, C. A., Gija, V., Nuyujukian, P., Ryu, S. I., Shenoy, K. V., Kier, R. J., Solzbacher, F., Harrison, R. R. 2008: 1752-1755
  • Signal processing challenges for neural prostheses IEEE SIGNAL PROCESSING MAGAZINE Linderman, M. D., Santhanam, G., Kemere, C. T., Gilja, V., O'Driscoll, S., Yu, B. M., Afshar, A., Ryu, S. I., Shenoy, K. V., Meng, T. H. 2008; 25 (1): 18-28
  • Brain-computer interfaces [from the guest editors]. IEEE Signal Processing Magazine. Sajda, P., Muller, K. R., Shenoy, K. V. 2008; 25: 16-17.
  • An Efficient Approximation for the Real-Time Implementation of the Mixture of Trajectory Models Decoder 2008 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE - INTELLIGENT BIOMEDICAL SYSTEMS (BIOCAS) Bishop, W., Yu, B. M., Santhanam, G., Afshar, A., Ryu, S. I., Shenoy, K. V. 2008: 133-136
  • Neural decoding of movements: From linear to nonlinear trajectory models NEURAL INFORMATION PROCESSING, PART I Yu, B. M., Cunningham, J. P., Shenoy, K. V., Sahani, M. 2008; 4984: 586-595
  • A Wireless Neural Interface for Chronic Recording 2008 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE - INTELLIGENT BIOMEDICAL SYSTEMS (BIOCAS) Harrison, R. R., Kier, R. J., Kim, S., Rieth, L., Warren, D. J., Ledbetter, N. M., Clark, G. A., Solzbacher, F., Chestek, C. A., Gilja, V., Nuyujukian, P., Ryu, S. I., Shenoy, K. V. 2008: 125-128
  • A Factor-Analysis decoder for high-performance neural prostheses 2008 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING, VOLS 1-12 Santhanam, G., Yu, B. M., Gilja, V., Ryu, S. I., Afshar, A., Sahani, M., Shenoy, K. V. 2008: 5208-5211
  • The Use of a Virtual Integration Environment for the Real-Time Implementation of Neural Decode Algorithms 2008 30TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-8 Bishop, W., Yu, B. M., Santhanam, G., Afshar, A., Ryu, S. I., Shenoy, K. V., Vogelstein, R. J., Beaty, J., Harshbarger, S. 2008: 628-633

    Abstract

    We have developed a virtual integration environment (VIE) for the development of neural prosthetic systems. This paper, the second of two companion articles, describes the use of the VIE as a common platform for the implementation of neural decode algorithms. In this paper, a linear filter decode and a recursive Bayesian algorithm are implemented as separate signal analysis modules of the VIE for the real-time decode of end effector trajectory. The process of implementing each algorithm is described and the real-time behavior as well as computational cost for each algorithm is examined. This is the first report of the real-time implementation of the Mixture of Trajectory Models decode [10]. These real-time algorithms can be easily interfaced with pre-existing modules of the VIE to control simulated and real devices.

    View details for Web of Science ID 000262404500158

    View details for PubMedID 19162734

  • Wireless neural signal acquisition with single low-power integrated circuit PROCEEDINGS OF 2008 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, VOLS 1-10 Harrison, R. R., Kier, R. J., Greger, B., Solzbacher, F., Chestek, C. A., Gija, V., Nuyujukian, P., Ryu, S. I., Shenoy, K. V. 2008: 1748-1751
  • HermesB: A continuous neural recording system for freely behaving primates IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Santhanam, G., Linderman, M. D., Gija, V., Afshar, A., Ryu, S. I., Meng, T. H., Shenoy, K. V. 2007; 54 (11): 2037-2050

    Abstract

    Chronically implanted electrode arrays have enabled a broad range of advances in basic electrophysiology and neural prosthetics. Those successes motivate new experiments, particularly, the development of prototype implantable prosthetic processors for continuous use in freely behaving subjects, both monkeys and humans. However, traditional experimental techniques require the subject to be restrained, limiting both the types and duration of experiments. In this paper, we present a dual-channel, battery-powered neural recording system with an integrated three-axis accelerometer for use with chronically implanted electrode arrays in freely behaving primates. The recording system called HermesB, is self-contained, autonomous, programmable, and capable of recording broadband neural (sampled at 30 kS/s) and acceleration data to a removable compact flash card for up to 48 h. We have collected long-duration data sets with HermesB from an adult macaque monkey which provide insight into time scales and free behaviors inaccessible under traditional experiments. Variations in action potential shape and root-mean square (RMS) noise are observed across a range of time scales. The peak-to-peak voltage of action potentials varied by up to 30% over a 24-h period including step changes in waveform amplitude (up to 25%) coincident with high acceleration movements of the head. These initial results suggest that spike-sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance. During physically active periods (defined by head-mounted accelerometer), significantly reduced 5-25-Hz local field potential (LFP) power and increased firing rate variability were observed. Using a threshold fit to LFP power, 93% of 403 5-min recording blocks were correctly classified as active or inactive, potentially providing an efficient tool for identifying different behavioral contexts in prosthetic applications. These results demonstrate the utility of the HermesB system and motivate using this type of system to advance neural prosthetics and electrophysiological experiments.

    View details for DOI 10.1109/TBME.2007.895753

    View details for Web of Science ID 000250449200014

    View details for PubMedID 18018699

  • Single-neuron stability during repeated reaching in macaque premotor cortex JOURNAL OF NEUROSCIENCE Chestek, C. A., Batista, A. P., Santhanam, G., Yu, B. M., Afshar, A., Cunningham, J. P., Gilja, V., Ryu, S. I., Churchland, M. M., Shenoy, K. V. 2007; 27 (40): 10742-10750

    Abstract

    Some movements that animals and humans make are highly stereotyped, repeated with little variation. The patterns of neural activity associated with repeats of a movement may be highly similar, or the same movement may arise from different patterns of neural activity, if the brain exploits redundancies in the neural projections to muscles. We examined the stability of the relationship between neural activity and behavior. We asked whether the variability in neural activity that we observed during repeated reaching was consistent with a noisy but stable relationship, or with a changing relationship, between neural activity and behavior. Monkeys performed highly similar reaches under tight behavioral control, while many neurons in the dorsal aspect of premotor cortex and the primary motor cortex were simultaneously monitored for several hours. Neural activity was predominantly stable over time in all measured properties: firing rate, directional tuning, and contribution to a decoding model that predicted kinematics from neural activity. The small changes in neural activity that we did observe could be accounted for primarily by subtle changes in behavior. We conclude that the relationship between neural activity and practiced behavior is reasonably stable, at least on timescales of minutes up to 48 h. This finding has significant implications for the design of neural prosthetic systems because it suggests that device recalibration need not be overly frequent, It also has implications for studies of neural plasticity because a stable baseline permits identification of nonstationary shifts.

    View details for DOI 10.1523/JNEUROSCI.0959-07.2007

    View details for Web of Science ID 000249981400012

    View details for PubMedID 17913908

  • Techniques for extracting single-trial activity patterns from large-scale neural recordings CURRENT OPINION IN NEUROBIOLOGY Churchland, M. M., Yu, B. M., Sahani, M., Shenoy, K. V. 2007; 17 (5): 609-618

    Abstract

    Large, chronically implanted arrays of microelectrodes are an increasingly common tool for recording from primate cortex and can provide extracellular recordings from many (order of 100) neurons. While the desire for cortically based motor prostheses has helped drive their development, such arrays also offer great potential to advance basic neuroscience research. Here we discuss the utility of array recording for the study of neural dynamics. Neural activity often has dynamics beyond that driven directly by the stimulus. While governed by those dynamics, neural responses may nevertheless unfold differently for nominally identical trials, rendering many traditional analysis methods ineffective. We review recent studies - some employing simultaneous recording, some not - indicating that such variability is indeed present both during movement generation and during the preceding premotor computations. In such cases, large-scale simultaneous recordings have the potential to provide an unprecedented view of neural dynamics at the level of single trials. However, this enterprise will depend not only on techniques for simultaneous recording but also on the use and further development of analysis techniques that can appropriately reduce the dimensionality of the data, and allow visualization of single-trial neural behavior.

    View details for DOI 10.1016/j.conb.2007.11.001

    View details for Web of Science ID 000252835100016

    View details for PubMedID 18093826

  • Free-paced high-performance brain-computer interfaces JOURNAL OF NEURAL ENGINEERING Achtman, N., Afshar, A., Santhanam, G., Yu, B. M., Ryu, S. I., Shenoy, K. V. 2007; 4 (3): 336-347

    Abstract

    Neural prostheses aim to improve the quality of life of severely disabled patients by translating neural activity into control signals for guiding prosthetic devices or computer cursors. We recently demonstrated that plan activity from premotor cortex, which specifies the endpoint of the upcoming arm movement, can be used to swiftly and accurately guide computer cursors to the desired target locations. However, these systems currently require additional, non-neural information to specify when plan activity is present. We report here the design and performance of state estimator algorithms for automatically detecting the presence of plan activity using neural activity alone. Prosthesis performance was nearly as good when state estimation was used as when perfect plan timing information was provided separately ( approximately 5 percentage points lower, when using 200 ms of plan activity). These results strongly suggest that a completely neurally-driven high-performance brain-computer interface is possible.

    View details for DOI 10.1088/1741-2560/4/3/018

    View details for Web of Science ID 000250181600025

    View details for PubMedID 17873435

  • Reference frames for reach planning in macaque dorsal premotor cortex JOURNAL OF NEUROPHYSIOLOGY Batista, A. P., Santhanam, G., Yu, B. M., Ryu, S. I., Afshar, A., Shenoy, K. V. 2007; 98 (2): 966-983

    Abstract

    When a human or animal reaches out to grasp an object, the brain rapidly computes a pattern of muscular contractions that can acquire the target. This computation involves a reference frame transformation because the target's position is initially available only in a visual reference frame, yet the required control signal is a set of commands to the musculature. One of the core brain areas involved in visually guided reaching is the dorsal aspect of the premotor cortex (PMd). Using chronically implanted electrode arrays in two Rhesus monkeys, we studied the contributions of PMd to the reference frame transformation for reaching. PMd neurons are influenced by the locations of reach targets relative to both the arm and the eyes. Some neurons encode reach goals using limb-centered reference frames, whereas others employ eye-centered reference fames. Some cells encode reach goals in a reference frame best described by the combined position of the eyes and hand. In addition to neurons like these where a reference frame could be identified, PMd also contains cells that are influenced by both the eye- and limb-centered locations of reach goals but for which a distinct reference frame could not be determined. We propose two interpretations for these neurons. First, they may encode reach goals using a reference frame we did not investigate, such as intrinsic reference frames. Second, they may not be adequately characterized by any reference frame.

    View details for DOI 10.1152/jn.00421.2006

    View details for Web of Science ID 000248601100036

    View details for PubMedID 17581846

  • Temporal complexity and heterogeneity of single-neuron activity in premotor and motor cortex JOURNAL OF NEUROPHYSIOLOGY Churchland, M. M., Shenoy, K. V. 2007; 97 (6): 4235-4257

    Abstract

    The relationship between neural activity in motor cortex and movement is highly debated. Although many studies have examined the spatial tuning (e.g., for direction) of cortical responses, less attention has been paid to the temporal properties of individual neuron responses. We developed a novel task, employing two instructed speeds, that allows meaningful averaging of neural responses across reaches with nearly identical velocity profiles. Doing so preserves fine temporal structure and reveals considerable complexity and heterogeneity of response patterns in primary motor and premotor cortex. Tuning for direction was prominent, but the preferred direction was frequently inconstant with respect to time, instructed-speed, and/or reach distance. Response patterns were often temporally complex and multiphasic, and varied with direction and instructed speed in idiosyncratic ways. A wide variety of patterns was observed, and it was not uncommon for a neuron to exhibit a pattern shared by no other neuron in our dataset. Response patterns of individual neurons rarely, if ever, matched those of individual muscles. Indeed, the set of recorded responses spanned a much higher dimensional space than would be expected for a model in which neural responses relate to a moderate number of factors-dynamic, kinematic, or otherwise. Complex responses may provide a basis-set representing many parameters. Alternately, it may be necessary to discard the notion that responses exist to "represent" movement parameters. It has been argued that complex and heterogeneous responses are expected of a recurrent network that produces temporally patterned outputs, and the present results would seem to support this view.

    View details for DOI 10.1152/jn.00095.2007

    View details for Web of Science ID 000247938200038

    View details for PubMedID 17376854

  • Mixture of trajectory models for neural decoding of goal-directed movements JOURNAL OF NEUROPHYSIOLOGY Yu, B. M., Kemere, C., Santhanam, G., Afshar, A., Ryu, S. I., Meng, T. H., Sahani, M., Shenoy, K. V. 2007; 97 (5): 3763-3780

    Abstract

    Probabilistic decoding techniques have been used successfully to infer time-evolving physical state, such as arm trajectory or the path of a foraging rat, from neural data. A vital element of such decoders is the trajectory model, expressing knowledge about the statistical regularities of the movements. Unfortunately, trajectory models that both 1) accurately describe the movement statistics and 2) admit decoders with relatively low computational demands can be hard to construct. Simple models are computationally inexpensive, but often inaccurate. More complex models may gain accuracy, but at the expense of higher computational cost, hindering their use for real-time decoding. Here, we present a new general approach to defining trajectory models that simultaneously meets both requirements. The core idea is to combine simple trajectory models, each accurate within a limited regime of movement, in a probabilistic mixture of trajectory models (MTM). We demonstrate the utility of the approach by using an MTM decoder to infer goal-directed reaching movements to multiple discrete goals from multi-electrode neural data recorded in monkey motor and premotor cortex. Compared with decoders using simpler trajectory models, the MTM decoder reduced the decoding error by 38 (48) percent in two monkeys using 98 (99) units, without a necessary increase in running time. When available, prior information about the identity of the upcoming reach goal can be incorporated in a principled way, further reducing the decoding error by 20 (11) percent. Taken together, these advances should allow prosthetic cursors or limbs to be moved more accurately toward intended reach goals.

    View details for DOI 10.1152/jn.00482.2006

    View details for Web of Science ID 000247933500055

    View details for PubMedID 17329627

  • Delay of movement caused by disruption of cortical preparatory activity JOURNAL OF NEUROPHYSIOLOGY Churchland, M. M., Shenoy, K. V. 2007; 97 (1): 348-359

    Abstract

    We tested the hypothesis that delay-period activity in premotor cortex is essential to movement preparation. During a delayed-reach task, we used subthreshold intracortical microstimulation to disrupt putative "preparatory" activity. Microstimulation led to a highly specific increase in reach reaction time. Effects were largest when activity was disrupted around the time of the go cue. Earlier disruptions, which presumably allowed movement preparation time to recover, had only a weak impact. Furthermore, saccadic reaction time showed little or no increase. Finally, microstimulation of nearby primary motor cortex, even when slightly suprathreshold, had little effect on reach reaction time. These findings provide the first evidence, of a causal and temporally specific nature, that activity in premotor cortex is fundamental to movement preparation. Furthermore, although reaction times were increased, the movements themselves were essentially unperturbed. This supports the suggestion that movement preparation is an active and actively monitored process and that movement can be delayed until inaccuracies are repaired. These results are readily interpreted in the context of the recently developed optimal-subspace hypothesis.

    View details for DOI 10.1152/jn.00808.2006

    View details for Web of Science ID 000243532900033

    View details for PubMedID 17005608

  • Hit and miss. Nature. Dell, H. 2007; 445:36.
  • Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach JOURNAL OF NEUROPHYSIOLOGY Churchland, M. M., Santhanam, G., Shenoy, K. V. 2006; 96 (6): 3130-3146

    Abstract

    Neurons in premotor and motor cortex show preparatory activity during an instructed-delay task. It has been suggested that such activity primarily reflects visuospatial aspects of the movement, such as target location or reach direction and extent. We asked whether a more dynamic feature, movement speed, is also reflected. Two monkeys were trained to reach at different speeds ("slow" or "fast," peak speed being approximately 50-100% higher for the latter) depending on target color. Targets were presented in seven directions and at two distances. Of 95 neurons with tuned delay-period activity, 95, 78, and 94% showed a significant influence of direction, distance, and instructed speed, respectively. Average peak modulations with respect to direction, distance and speed were 18, 10, and 11 spikes/s. Although robust, modulations of firing rate with target direction were not necessarily invariant: for 45% of neurons, the preferred direction depended significantly on target distance and/or instructed speed. We collected an additional dataset, examining in more detail the effect of target distance (5 distances from 3 to 12 cm in 2 directions). Of 41 neurons with tuned delay-period activity, 85, 83, and 98% showed a significant impact of direction, distance, and instructed speed. Statistical interactions between the effects of distance and instructed speed were common, but it was nevertheless clear that distance "tuning" was not in general a simple consequence of speed tuning. We conclude that delay-period preparatory activity robustly reflects a nonspatial aspect of the upcoming reach. However, it is unclear whether the recorded neural responses conform to any simple reference frame, intrinsic or extrinsic.

    View details for DOI 10.1152/jn.00307.2006

    View details for Web of Science ID 000242177800033

    View details for PubMedID 16855111

  • Neural variability in premotor cortex provides a signature of motor preparation JOURNAL OF NEUROSCIENCE Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G., Shenoy, K. V. 2006; 26 (14): 3697-3712

    Abstract

    We present experiments and analyses designed to test the idea that firing rates in premotor cortex become optimized during motor preparation, approaching their ideal values over time. We measured the across-trial variability of neural responses in dorsal premotor cortex of three monkeys performing a delayed-reach task. Such variability was initially high, but declined after target onset, and was maintained at a rough plateau during the delay. An additional decline was observed after the go cue. Between target onset and movement onset, variability declined by an average of 34%. This decline in variability was observed even when mean firing rate changed little. We hypothesize that this effect is related to the progress of motor preparation. In this interpretation, firing rates are initially variable across trials but are brought, over time, to their "appropriate" values, becoming consistent in the process. Consistent with this hypothesis, reaction times were longer if the go cue was presented shortly after target onset, when variability was still high, and were shorter if the go cue was presented well after target onset, when variability had fallen to its plateau. A similar effect was observed for the natural variability in reaction time: longer (shorter) reaction times tended to occur on trials in which firing rates were more (less) variable. These results reveal a remarkable degree of temporal structure in the variability of cortical neurons. The relationship with reaction time argues that the changes in variability approximately track the progress of motor preparation.

    View details for DOI 10.1523/JNEUROSCI.3762-05.2006

    View details for Web of Science ID 000236552400012

    View details for PubMedID 16597724

  • Multiday electrophysiological recordings from freely behaving primates 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 Gilja, V., Linderman, M. D., Santhanam, G., Afshar, A., Shenoy, K. V. 2006: 5723-5726
  • Multiday electrophysiological recordings from freely behaving primates. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Gilja, V., Linderman, M. D., Santhanam, G., Afshar, A., Ryu, S., Meng, T. H., Shenoy, K. V. 2006; 1: 5643-5646

    Abstract

    Continuous multiday broadband neural data provide a means for observing effects at fine timescales over long periods. In this paper we present analyses on such data sets to demonstrate neural correlates for physically active and inactive time periods, as defined by the response of a head-mounted accelerometer. During active periods, we found that 5-25 Hz local field potential (LFP) power was significantly reduced, firing rate variability increased, and firing rates have greater temporal correlation. Using a single threshold fit to LFP power, 93% of the 403 5 minute blocks tested were correctly classified as active or inactive (as labeled by thresholding each block's maximal accelerometer magnitude). These initial results motivate the use of such data sets for testing neural prosthetics systems and for finding the neural correlates of natural behaviors.

    View details for PubMedID 17947159

  • An autonomous, broadband, multi-channel neural recording system for freely behaving primates. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Linderman, M. D., Gilja, V., Santhanam, G., Afshar, A., Ryu, S., Meng, T. H., Shenoy, K. V. 2006; 1: 1212-1215

    Abstract

    Successful laboratory proof-of-concept experiments with neural prosthetic systems motivate continued algorithm and hardware development. For these efforts to move beyond traditional fixed laboratory setups, new tools are needed to enable broadband, multi-channel, long duration neural recording from freely behaving primates. In this paper we present a dual-channel, battery powered, neural recording system with integrated 3-axis accelerometer for use with chronically implanted electrode arrays. The recording system, called HermesB, is self-contained, autonomous, programmable and capable of recording broadband neural and head acceleration data to a removable compact flash card for up to 48 hours.

    View details for PubMedID 17946450

  • Increasing the performance of cortically-controlled prostheses. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Shenoy, K. V., Santhanam, G., Ryu, S. I., Afshar, A., Yu, B. M., Gilja, V., Linderman, M. D., Kalmar, R. S., Cunningham, J. P., Kemere, C. T., Batista, A. P., Churchland, M. M., Meng, T. H. 2006: 6652-6656

    Abstract

    Neural prostheses have received considerable attention due to their potential to dramatically improve the quality of life of severely disabled patients. Cortically-controlled prostheses are able to translate neural activity from cerebral cortex into control signals for guiding computer cursors or prosthetic limbs. Non-invasive and invasive electrode techniques can be used to measure neural activity, with the latter promising considerably higher levels of performance and therefore functionality to patients. We review here some of our recent experimental and computational work aimed at establishing a principled design methodology to increase electrode-based cortical prosthesis performance to near theoretical limits. Studies discussed include translating unprecedentedly brief periods of "plan" activity into high information rate (6.5 bits/s)control signals, improving decode algorithms and optimizing visual target locations for further performance increases, and recording from chronically implanted arrays in freely behaving monkeys to characterize neuron stability. Taken together, these results should substantially increase the clinical viability of cortical prostheses.

    View details for PubMedID 17959477

  • Integrated semiconductor optical sensors for chronic, minimally-invasive imaging of brain function. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Lee, T. T., Levi, O., Cang, J., Kaneko, M., Stryker, M. P., Smith, S. J., Shenoy, K. V., Harris, J. S. 2006; 1: 1025-1028

    Abstract

    Intrinsic optical signal (IOS) imaging is a widely accepted technique for imaging brain activity. We propose an integrated device consisting of interleaved arrays of gallium arsenide (GaAs) based semiconductor light sources and detectors operating at telecommunications wavelengths in the near-infrared. Such a device will allow for long-term, minimally invasive monitoring of neural activity in freely behaving subjects, and will enable the use of structured illumination patterns to improve system performance. In this work we describe the proposed system and show that near-infrared IOS imaging at wavelengths compatible with semiconductor devices can produce physiologically significant images in mice, even through skull.

    View details for PubMedID 17946016

  • Optimal target placement for neural communication prostheses. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Cunningham, J. P., Yu, B. M., Shenoy, K. V. 2006; 1: 2912-2915

    Abstract

    Neural prosthetic systems have been designed to estimate continuous reach trajectories as well as discrete reach targets. In the latter case, reach targets are typically decoded from neural activity during an instructed delay period, before the reach begins. We have recently characterized the decoding speed and accuracy achievable by such a system. The results were obtained using canonical target layouts, independent of the tuning properties of the neurons available. Here we seek to increase decode accuracy by judiciously selecting the locations of the reach targets based on the characteristics of the neural population at hand. We present an optimal target placement algorithm that approximately maximizes decode accuracy with respect to target locations. Using maximum likelihood decoding, the optimal target placement algorithm yielded up to 11 and 12% improvement for two and sixteen targets, respectively. For four and eight targets, gains were more modest (5 and 3%, respectively) as the target layouts found by the algorithm closely resembled the canonical layouts. Thus, the algorithm can serve not only to find target layouts that outperform canonical layouts, but it can also confirm or help select among multiple canonical layouts. These results indicate that the optimal target placement algorithm is a valuable tool for designing high-performance prosthetic systems.

    View details for PubMedID 17945745

  • Neural recording stability of chronic electrode arrays in freely behaving primates. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Linderman, M. D., Gilja, V., Santhanam, G., Afshar, A., Ryu, S., Meng, T. H., Shenoy, K. V. 2006; 1: 4387-4391

    Abstract

    Chronically implanted electrode arrays have enabled a broad range of advances, particularly in the field of neural prosthetics. Those successes motivate development of prototype implantable prosthetic processors for long duration, continuous use in freely behaving subjects. However, traditional experimental protocols have provided limited information regarding the stability of the electrode arrays and their neural recordings. In this paper we present preliminary results derived from long duration neural recordings in a freely behaving primate which show variations in action potential shape and RMS noise across a range of time scales. These preliminary results suggest that spike sorting algorithms can no longer assume stable neural signals and will need to transition to adaptive signal processing methodologies to maximize performance.

    View details for PubMedID 17946626

  • Hidden Markov models for spatial and temporal estimation for prosthetic control. Abstract Viewer / Itinerary Planner. Kemere, C., Yu, B. M., Santhanam, G., Ryu, S. I., Afshar, A., Meng, T. H., Shenoy, Krishna, V. 2006
  • Brain-Machine Interfaces introduction: Brain-machine interfaces promise to aid paralyzed patients by re-routing movement-related signals around damaged parts of the nervous system. A new study in Nature demonstrates a human with spinal injury manipulating a screen cursor and robotic devices by thought alone (Hochberg et al. Nature 442:164-171, 2006). Implanted electrodes in his motor cortex recorded neural activity, and translated it into movement commands. A second study, in monkeys, shows that brain-machine interfaces can operate at high speed, greatly increasing their clinical potential (Santhanam et al. Nature 442:195-198, 2006). This Nature Web Focus includes exclusive interviews and video footage of experiments, alongside papers that paved the way for these recent advances. Shenoy, K. V. 2006; 442: 164-171, 195-198
  • Generating complex repeatable patterns of activity by gain modulating network neurons. Abstract Viewer / Itinerary Planner. Schaffer, E. S., Rajan, K., Churchland, M. M., Shenoy, K. V., Abbott, L. F. 2006
  • Multiday electrophysiological recordings from freely behaving primates using an autonomous, multi-channel neural system. Abstract Viewer / Itinerary Planner. Gilja, V., Linderman, M. D., Santhanam, G., Afshar, A., Ryu, S. I., Meng, T. H., Shenoy, Krishna, V. 2006
  • Neurons to Silicon: Implantable Prosthesis Processor. International Solid State Circuits Conference (ISSCC) O'Driscoll, S., Meng, T. H., Shenoy, K. V., Kemere, C. 2006: 552-553 & 672.
  • Expectation propagation for inference in non-linear dynamical models with Poisson observations. Nonlinear Statistical Signal Processing Workshop Yu, B. M., Shenoy, K. V., Sahani, M. 2006
  • Neurological disorders: Mind over machine. Nature Reviews Neuroscience Barton, S. 2006; 7: 682-683.
  • A central source of movement variability Neuron. Churchland, M. M., Afshar, A., Shenoy, K. V. 2006; 52: 1085-1096.
  • Optimal target placement for neural communication prostheses. Abstract Viewer / Itinerary Planner. Atlanta Cunningham, J. P., Yu, B. M., Shenoy, K. V. 2006
  • Influence of eye position on end-point decoding accuracy in dorsal premotor cortex. Abstract Viewer / Itinerary Planner. Batista, A. P., Yu, B. M., Santhanam, G., Ryu, S. I., Afshar, A., Shenoy, K. V. 2006
  • The relationship between PMd neural activity and reaching behavior is stable in highly trained macaques. Abstract Viewer / Itinerary Planner. Chestek, C. A., Batista, A. P., Yu, B. M., Santhanam, G., Ryu, S. I., Afshar, A., Shenoy, Krishna, V. 2006
  • Factor analysis with Poisson output. Technical Report NPSL-TR-06-1. Santhanam, G., Yu, B. M., Shenoy, K. V., Sahani, M. 2006
  • Bionic brains become a reality Hopkin, M. 2006
  • Is this the bionic man Nature Shenoy, K. V. 2006; 442:109
  • Neuroscience: Converting thoughts into action. Nature Scott, S. H. 2006; 442: 141-142.
  • Preparing for speed. Focus on: Preparatory activity in premotor and motor cortex reflects the speed of the upcoming reach. Journal of Neurophysiology Cisek, P. 2006; 96: 2842-2843.
  • Neuroprosthetics: In search of the sixth sense. Nature Abbott, A. 2006; 442: 125-127.
  • Neural rklecording stability of chronic electrode arrays in freely behaving primates 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 Linderman, M. D., Gilja, V., Santhanam, G., Afshar, A., Ryu, S., Meng, T. H., Shenoy, K. V. 2006: 3784-3788
  • Optimal target placement for neural communication prostheses 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 Cunningham, J. P., Yu, B. M., Shenoy, K. V. 2006: 1063-1066
  • Integrated semiconductor optical sensors for chronic, minimally-invasive imaging of brain function 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 Lee, T. T., Levi, O., Cang, J., Kaneko, M., Stryker, M. P., Smith, S. J., Shenoy, K. V., Harris, J. S. 2006: 2443-2446
  • An autonomous, broadband, multi-channel neural recording system for freely behaving primates 2006 28TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-15 Linderman, M. D., Gilja, V., Santhanam, G., Afshar, A., Ryu, S., Meng, T. H., Shenoy, K. V. 2006: 3780-3783
  • Power feasibility of implantable digital spike sorting circuits for neural prosthetic systems IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING Zumsteg, Z. S., Kemere, C., O'Driscoll, S., Santhanam, G., Ahmed, R. E., Shenoy, K. V., Meng, T. H. 2005; 13 (3): 272-279

    Abstract

    A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that state-of-the-art spike sorting algorithms are not only feasible using modern complementary metal oxide semiconductor very large scale integration processes, but may represent the best option for extracting large amounts of data in implantable neural prosthetic interfaces.

    View details for DOI 10.1109/TNSRE.2005.854307

    View details for Web of Science ID 000231969500004

    View details for PubMedID 16200751

  • A high performance neurally-controlled cursor positioning system 2005 2ND INTERNATINOAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., Shenoy, K. V. 2005: 494-500
  • Extracting dynamical structure embedded in neural activity. 2005 Abstract Viewer/Itinerary Planner. Sahani, M., Yu, B. M., Afshar, G., Santhanam, G., Ryu, S. I., Shenoy, K. V. 2005
  • PMd delay activity during rapid sequential movement plans. 2005 Abstract Viewer/Itinerary Planner. Kalmar, R. S., Gilja, V., Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., Shenoy, Krishna, V. 2005
  • Trial-by-trial mean normalization improves plan period reach target decoding. 2005 Abstract Viewer/Itinerary Planner. Gilja, V., Kalmar, R. S., Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., Shenoy, Krishna, V. 2005
  • Free-paced target estimation in a delayed reach task. 2005 Abstract Viewer/Itinerary Planner. Afshar, A., Achtman, N., Santhanam, G., Ryu, S. I., Yu, B. M., Shenoy, K. V. 2005
  • Complex patterns of motor cortex activity during reaches at different speeds. 2005 Abstract Viewer/Itinerary Planner. Churchland, M. M., Shenoy, K. V. 2005
  • Heterogeneous coordinate frames for reaching in macaque PMd. 2005 Abstract Viewer/Itinerary Planner. Batista, A. P., Santhanam, G., Yu, B. M., Ryu, S. I., Afshar, A., Shenoy, K. V. 2005
  • Motor preparation and settling activity in PMd. Neural Control of Movement (NCM) Annual Meeting Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G., Shenoy, K. V. 2005
  • Mixture of trajectory models for neural decoding of goal-directed movements. 2005 Abstract Viewer/Itinerary Planner. Yu, B. M., Kemere, C., Santhanam, G., Afshar, A., Ryu, S. I., Meng, T. H., Shenoy, Krishna, V. 2005
  • Intra-cortical communication prosthesis design. 2005 Abstract Viewer/Itinerary Planner. Santhanam, G., Ryu, S. I., Yu, B. M., Afshar, A., Afshar, K. V. 2005
  • Model-based neural decoding of reaching movements: A maximum likelihood approach IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING Kemere, C., Shenoy, K. V., Meng, T. H. 2004; 51 (6): 925-932

    Abstract

    A new paradigm for decoding reaching movements from the signals of an ensemble of individual neurons is presented. This new method not only provides a novel theoretical basis for the task, but also results in a significant decrease in the error of reconstructed hand trajectories. By using a model of movement as a foundation for the decoding system, we show that the number of neurons required for reconstruction of the trajectories of point-to-point reaching movements in two dimensions can be halved. Additionally, using the presented framework, other forms of neural information, specifically neural "plan" activity, can be integrated into the trajectory decoding process. The decoding paradigm presented is tested in simulation using a database of experimentally gathered center-out reaches and corresponding neural data generated from synthetic models.

    View details for DOI 10.1109/TBME.2004.826675

    View details for Web of Science ID 000221578000008

    View details for PubMedID 15188860

  • An extensible infrastructure for fully automated spike sorting during online experiments PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 Santhanam, G., Sahani, M., Ryu, S. I., Shenoy, K. V. 2004; 26: 4380-4384
  • An extensible infrastructure for fully automated spike sorting during online experiments. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Santhanam, G., Sahani, M., Ryu, S., Shenoy, K. 2004; 6: 4380-4384

    Abstract

    When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as "spike sorting." In a number of experiments, notably those that involve direct neuroprosthetic control of an effector, this cell-by-cell classification of the incoming signal must be achieved in real time. Several commercial offerings are available for this task, but all of these require some manual supervision per electrode, making each scheme cumbersome with large electrode counts. We present a new infrastructure that leverages existing unsupervised algorithms to sort and subsequently implement the resulting signal classification rules for each electrode using a commercially available Cerebus neural signal processor. We demonstrate an implementation of this infrastructure to classify signals from a cortical electrode array, using a probabilistic clustering algorithm (described elsewhere). The data were collected from a rhesus monkey performing a delayed center-out reach task. We used both sorted and unsorted (thresholded) action potentials from an array implanted in pre-motor cortex to "predict" the reach target, a common decoding operation in neuroprosthetic research. The use of sorted spikes led to an improvement in decoding accuracy of between 3.6 and 6.4%.

    View details for PubMedID 17271276

  • Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Zumsteg, Z. S., Ahmed, R. E., Santhanam, G., Shenoy, K. V., Meng, T. H. 2004; 6: 4237-4240

    Abstract

    A new class of neural prosthetic systems aims to assist disabled patients by translating cortical neural activity into control signals for prosthetic devices. Based on the success of proof-of-concept systems in the laboratory, there is now considerable interest in increasing system performance and creating implantable electronics for use in clinical systems. A critical question that impacts system performance and the overall architecture of these systems is whether it is possible to identify the neural source of each action potential (spike sorting) in real-time and with low power. Low power is essential both for power supply considerations and heat dissipation in the brain. In this paper we report that several state-of-the-art spike sorting algorithms implemented in modern CMOS VLSI processes are expected to be power realistic.

    View details for PubMedID 17271239

  • Model-based decoding of reaching movements for prosthetic systems. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Kemere, C., Santhanam, G., Yu, B. M., Ryu, S., Meng, T., Shenoy, K. V. 2004; 6: 4524-4528

    Abstract

    Model-based decoding of neural activity for neuroprosthetic systems has been shown, in simulation, to provide significant gain over traditional linear filter approaches. We tested the model-based decoding approach with real neural and behavioral data and found a 18% reduction in trajectory reconstruction error compared with a linear filter. This corresponds to a 40% reduction in the number of neurons required for equivalent performance. The model-based approach further permits the combination of target-tuned plan activity with movement activity. The addition of plan activity reduced reconstruction error by 23% relative to the linear filter, corresponding to 55% reduction in the number of neurons required. Taken together, these results indicate that a decoding algorithm employing a prior model of reaching kinematics can substantially improve trajectory estimates, thereby improving prosthetic system performance.

    View details for PubMedID 17271312

  • Improving neural prosthetic system performance by combining plan and peri-movement activity. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Yu, B. M., Ryu, S. I., Santhanam, G., Churchland, M. M., Shenoy, K. V. 2004; 6: 4516-4519

    Abstract

    While most neural prosthetic systems to date estimate arm movements based solely on the activity prior to reaching movements during a delay period (plan activity) or solely on the activity during reaching movements (peri-movement activity), we show that decode classification can be improved by 56% and 71% respectively by using both types of activity together. We recorded from the pre-motor cortex of a rhesus monkey performing a delayed-reach task to one of seven targets. We found that taking into account the time-varying structure in peri-movement activity further improved performance by 15%, while doing the same for plan activity did not improve performance. We also found low correlations in activity between pairs of simultaneously-recorded units and across time periods within a given trial condition. These results show that decode performance can be significantly improved by combining information from the plan and peri-movement periods, and that there is nearly no loss in performance when assuming independence between units and across tune periods within a given trial condition.

    View details for PubMedID 17271310

  • Reaction time and the time-course of cortical pre-motor processing. Soc. for Neurosci. Churchland, M. M., Yu, B., Ryu, S. I., Santhanam, G., Shenoy, K. V. 2004
  • Coordinate frames for reaching in macaque dorsal premotor cortex (PMd). Soc. for Neurosci. Batista, A. P., Yu, B. M., Santhanam, G., Ryu, S. I., Shenoy, K. V. 2004
  • Contribution of motor preparation and execution noise to goal-irrelevant movement variability. Soc. for Neurosci. Afshar, A., hurchland, M. M., Shenoy, K. V. 2004
  • Improving neural prosthetic system performance by combining plan and peri-movement activity. Soc. for Neurosci. Yu, B. M., Ryu, S. I., Santhanam, G., Churchland, M. M., Shenoy, K. V. 2004
  • Changes in reaction time induced by microstimulation in PMd. Soc. for Neurosci. Shenoy, K. V., Churchland, M. M. 2004
  • High speed neural prosthetic icon positioning. Soc. for Neurosci. Ryu, S. I., Santhanam, G., Yu, B. M., Shenoy, K. V. 2004
  • High information transmission rates in a neural prosthetic system. Soc. for Neurosci. Santhanam, G., Ryu, S. I., Yu, B. M., Shenoy, K. V. 2004
  • Reconstruction of arm trajectories from plan and peri-movement motor cortical activity. Soc. for Neurosci. Kemere, C., Santhanam, G., Ryu, S. I., Yu, B. M., Meng, T. H., Shenoy, K. V. 2004
  • Local field potential measurement with low-power analog integrated circuit PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 Harrison, R. R., Santhanam, G., Shenoy, K. V. 2004; 26: 4067-4070
  • Validation of adaptive threshold spike detector for neural recording PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 Watkins, P. T., Santhanam, G., Shenoy, K. V., Harrison, R. R. 2004; 26: 4079-4082
  • Validation of adaptive threshold spike detector for neural recording. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Watkins, P. T., Santhanam, G., Shenoy, K. V., Harrison, R. R. 2004; 6: 4079-4082

    Abstract

    We compare the performance of algorithms for automatic spike detection in neural recording applications. Each algorithm sets a threshold based on an estimate of the background noise level. The adaptive spike detection algorithm is suitable for implementation in analog VLSI; results from a proof-of-concept chip using neural data are presented. We also present simulation results of algorithm performance on neural data and compare it to other methods of threshold level adjustment based on the root-mean-square (rms) voltage measured over a finite window. We show that the adaptive spike detection algorithm measures the background noise level accurately despite the presence of large-amplitude action potentials and multi-unit hash. Simulation results enable us to optimize the algorithm parameters, leading to an improved spike detector circuit that is currently being developed.

    View details for PubMedID 17271196

  • Power feasibility of implantable digital spike-sorting circuits for neural prosthetic systems PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 Zumsteg, Z. S., Ahmed, R. E., Santhanam, G., Shenoy, K. V., Meng, T. H. 2004; 26: 4237-4240
  • Improving neural prosthetic system performance by combining plan and peri-movement activity PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 Yu, B. A., Ryu, S. I., Santhanam, G., Churchland, M. M., Shenoy, K. V. 2004; 26: 4516-4519
  • Model-based decoding of reaching movements for prosthetic systems PROCEEDINGS OF THE 26TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7 Kemere, C., Santhanam, G., Yu, B. M., Ryu, S., Meng, T., Shenoy, K. V. 2004; 26: 4524-4528
  • Local field potential measurement with low-power analog integrated circuit. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference Harrison, R. R., Santhanam, G., Shenoy, K. V. 2004; 6: 4067-4070

    Abstract

    Local field potentials (LFPs) in the brain are an important source of information for basic research and clinical (i.e., neuroprosthetic) applications. The energy contained in certain bands of LFPs in the 10-100 Hz range has been shown to correlate with specific arm movement parameters in nonhuman primates. In the near future, implantable devices will need to transmit neural information from hundreds of microelectrodes, and transcutaneous data transfer will become a significant bottleneck. Here we present a low-power, fully-integrated circuit that performs on-site data reduction by isolating LFPs and measuring their signal energy. The resulting analog VLSI circuit consumes 586 microm x 79 microm of silicon area and dissipates only 5 nanowatts of power. We show that the chip performs similarly to state-of-the-art signal processing algorithms.

    View details for PubMedID 17271193

  • Methods for estimating neural step sequences in neural prosthetic applications 1ST INTERNATIONAL IEEE EMBS CONFERENCE ON NEURAL ENGINEERING 2003, CONFERENCE PROCEEDINGS Santhanam, G., Shenoy, K. V. 2003: 344-347
  • Movement speed alters distance tuning of plan activity in monkey pre-motor cortex. Soc. for Neurosci. Churchland, M. M., Shenoy, K. V. 2003
  • Local field potential activity varies with reach distance, direction, and speed in monkey pre-motor cortex Soc. for Neurosci. G., Santhanam, Churchland, M. M., Sahani, M., Shenoy, K. V. 2003
  • Neural prosthetic control signals from plan activity. NeuroReport Shenoy, K. V., Meeker, D., Cao, S., Kureshi, S. A., Pesaran, B., Mitra, P. 2003; 14: 591-596.
  • Influence of movement speed on plan activity in monkey pre-motor cortex and implications for high-performance neural prosthetic system design PROCEEDINGS OF THE 25TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-4 Shenoy, K. V., Churchland, M. M., Santhanam, G., Yu, B. M., Ryu, S. I. 2003; 25: 1897-1900
  • Pursuit speed compensation in cortical area MSTd JOURNAL OF NEUROPHYSIOLOGY Shenoy, K. V., Crowell, J. A., Andersen, R. A. 2002; 88 (5): 2630-2647

    Abstract

    When we move forward the visual images on our retinas expand. Humans rely on the focus, or center, of this expansion to estimate their direction of self-motion or heading and, as long as the eyes are still, the retinal focus corresponds to the heading. However, smooth pursuit eye movements add visual motion to the expanding retinal image and displace the focus of expansion. In spite of this, humans accurately judge their heading during pursuit eye movements even though the retinal focus no longer corresponds to the heading. Recent studies in macaque suggest that correction for pursuit may occur in the dorsal aspect of the medial superior temporal area (MSTd); neurons in this area are tuned to the retinal position of the focus and they modify their tuning to partially compensate for the focus shift caused by pursuit. However, the question remains whether these neurons shift focus tuning more at faster pursuit speeds, to compensate for the larger focus shifts created by faster pursuit. To investigate this question, we recorded from 40 MSTd neurons while monkeys made pursuit eye movements at a range of speeds across simulated self- or object motion displays. We found that most MSTd neurons modify their focus tuning more at faster pursuit speeds, consistent with the idea that they encode heading and other motion parameters regardless of pursuit speed. Across the population, the median rate of compensation increase with pursuit speed was 51% as great as required for perfect compensation. We recorded from the same neurons in a simulated pursuit condition, in which gaze was fixed but the entire display counter-rotated to produce the same retinal image as during real pursuit. This condition yielded the result that retinal cues contribute to pursuit compensation; the rate of compensation increase was 30% of that required for accurate encoding of heading. The difference between these two conditions was significant (P < 0.05), indicating that extraretinal cues also contribute significantly. We found a systematic antialignment between preferred pursuit and preferred visual motion directions. Neurons may use this antialignment to combine retinal and extraretinal compensatory cues. These results indicate that many MSTd neurons compensate for pursuit velocity, pursuit direction as previously reported and pursuit speed, and further implicate MSTd as a critical stage in the computation of egomotion.

    View details for DOI 10.1152/jn.00002.2001

    View details for Web of Science ID 000179080900042

    View details for PubMedID 12424299

  • Decoding of plan and peri-movement neural signals in prosthetic systems 2002 IEEE WORKSHOP ON SIGNAL PROCESSING SYSTEMS Kemere, C. T., Santhanam, G., Yu, B. M., Shenoy, K. V., Meng, T. H. 2002: 276-283
  • Pursuit-Speed Compensation in Cortical Area MSTd. Journal of Neurophysiology. Shenoy, K. V., Crowell, J., Andersen, R. A. 2002; 88: 2630-2647.
  • Response of MSTd neurons to simulated 3D-orientation of rotating planes. Journal of Neurophysiology Sugihara, H., Murakami, I., Shenoy, K. V., Andersen, R. A., Komatsu, H. 2002; 87: 273-285.
  • Cognitive control signals for prosthetic systems. Soc. For Neurosci. Meeker, D., Shenoy, K. V., Cao, S., Pesaran, B., Scherberger, H., Jarvis, M. 2001; 27
  • Neural mechanisms for self-motion perception in area MST. International review of neurobiology Andersen, R. A., Shenoy, K. V., Crowell, J. A., BRADLEY, D. C. 2000; 44: 219-233

    View details for PubMedID 10605648

  • Toward Adaptive Control of Neural Prosthetics by Parietal Cortex. Neural Information and Coding Workshop. Meeker, D., Shenoy, K. V., Kureshi, S., Cao, S., Burdick, J., Pesaran, B. 2000
  • Influence of gaze rotation on the visual response of primate MSTd neurons JOURNAL OF NEUROPHYSIOLOGY Shenoy, K. V., BRADLEY, D. C., Andersen, R. A. 1999; 81 (6): 2764-2786

    Abstract

    When we move forward, the visual image on our retina expands. Humans rely on the focus, or center, of this expansion to estimate their direction of heading and, as long as the eyes are still, the retinal focus corresponds to the heading. However, smooth rotation of the eyes adds nearly uniform visual motion to the expanding retinal image and causes a displacement of the retinal focus. In spite of this, humans accurately judge their heading during pursuit eye movements and during active, smooth head rotations even though the retinal focus no longer corresponds to the heading. Recent studies in macaque suggest that correction for pursuit may occur in the dorsal aspect of the medial superior temporal area (MSTd) because these neurons are tuned to the retinal position of the focus and they modify their tuning during pursuit to compensate partially for the focus shift. However, the question remains whether these neurons also shift focus tuning to compensate for smooth head rotations that commonly occur during gaze tracking. To investigate this question, we recorded from 80 MSTd neurons while monkeys tracked a visual target either by pursuing with their eyes or by vestibulo-ocular reflex cancellation (VORC; whole-body rotation with eyes fixed in head and head fixed on body). VORC is a passive, smooth head rotation condition that selectively activates the vestibular canals. We found that neurons shift their focus tuning in a similar way whether focus displacement is caused by pursuit or by VORC. Across the population, compensation averaged 88 and 77% during pursuit and VORC, respectively (tuning shift divided by the retinal focus to true heading difference). Moreover the degree of compensation during pursuit and VORC was correlated in individual cells (P < 0.001). Finally neurons that did not compensate appreciably tended to be gain-modulated during pursuit and VORC and may constitute an intermediate stage in the compensation process. These results indicate that many MSTd cells compensate for general gaze rotation, whether produced by eye-in-head or head-in-world rotation, and further implicate MSTd as a critical stage in the computation of heading. Interestingly vestibular cues present during VORC allow many cells to compensate even though humans do not accurately judge their heading in this condition. This suggests that MSTd may use vestibular information to create a compensated heading representation within at least a subpopulation of cells, which is accessed perceptually only when additional cues related to active head rotations are also present.

    View details for Web of Science ID 000081005800017

    View details for PubMedID 10368396

  • The contributions of vestibular signals to the representations of space in the posterior parietal cortex OTOLITH FUNCTION IN SPATIAL ORIENTATION AND MOVEMENT Andersen, R. A., Shenoy, K. V., SNYDER, L. H., BRADLEY, D. C., Crowell, J. A. 1999; 871: 282-292

    Abstract

    Vestibular signals play an important role in spatial orientation, perception of object location, and control of self-motion. Prior physiological research on vestibular information processing has focused on brainstem mechanisms; relatively little is known about the processing of vestibular information at the level of the cerebral cortex. Recent electrophysiological experiments examining the use of vestibular canal signals in two different perceptual tasks are described: computation of self motion and localization of visual stimuli in a world-centered reference frame. These two perceptual functions are mediated by different parts of the posterior parietal cortex, the former in the dorsal aspect of the medial superior temporal area (MSTd) and the latter in area 7a.

    View details for Web of Science ID 000081273000022

    View details for PubMedID 10372079

  • Toward prosthetic systems controlled by parietal cortex. Soc. For Neurosci. Shenoy, K. V., Kureshi, S. A., Meeker, D., Gillikin, B. L., Dubowitz, D. J., Batista, A. P. 1999
  • Influence of pursuit speed on the representation of heading in macaque MSTd. European Conf. on Visual Percep. Shenoy, K. V., Crowell, J. A., Andersen, R. A. 1999
  • Prior visual motion affects self-motion judgments during eye movements. OVS/ARVO Crowell, J. A., Shenoy, K. V., Andersen, R. A. 1999; 40
  • Influence of gaze rotation on the visual response of primate MSTd neurons. Journal of Neurophysiology Shenoy, K. V., Bradley, D. C., Andersen, R. A. 1999; 81: 2764-2786.
  • Visual self-motion perception during head turns NATURE NEUROSCIENCE Crowell, J. A., Banks, M. S., Shenoy, K. V., Andersen, R. A. 1998; 1 (8): 732-737

    Abstract

    Extra-retinal information is critical in the interpretation of visual input during self-motion. Turning our eyes and head to track objects displaces the retinal image but does not affect our ability to navigate because we use extra-retinal information to compensate for these displacements. We showed observers animated displays depicting their forward motion through a scene. They perceived the simulated self-motion accurately while smoothly shifting the gaze by turning the head, but not when the same gaze shift was simulated in the display; this indicates that the visual system also uses extra-retinal information during head turns. Additional experiments compared self-motion judgments during active and passive head turns, passive rotations of the body and rotations of the body with head fixed in space. We found that accurate perception during active head turns is mediated by contributions from three extra-retinal cues: vestibular canal stimulation, neck proprioception and an efference copy of the motor command to turn the head.

    View details for Web of Science ID 000077323400019

    View details for PubMedID 10196591

  • Selectivity of neurons to the 3D orientation of a rotating plane in area MSTd of the monkey. Soc. for Neurosci. Sugihara, H., Murakami, I., Komatsu, H., Shenoy, K. V., Andersen, R. A. 1998; 24
  • Retinal and extra-retinal motion signals both affect the extent of gaze-shift compensation. IOVS/ARVO Crowell, J. A., Maxwell, M. A., Shenoy, K. V., Andersen, R. A. 1998; 39
  • Neurons in area MSTd of the monkey have a selectivity to the 3D orientation of a rotating plane. Japan Soc. for Neurosci. Sugihara, H., Murakami, I., Komatsu, H., Shenoy, K. V., Andersen, R. A. 1998
  • The influence of pursuit speed upon the representation of heading in Macaque cortical area MSTd. for Neurosci. Abstracts: Shenoy, K. V., Crowell, J. A., Andersen, R. A. 1998; 24
  • Perception of heading is a brain in the neck. Nature Neuroscience Warren, W. H. 1998; 1: 647-649.
  • Visual self-motion perception during head turns. Nature Neuroscience Crowell, J. A., Banks, M. S., Shenoy, K. V., Andersen, R. A. 1998; 1: 732-737.
  • Perception and neural representation of heading during gaze-rotation. Soc. for Neurosci. Abstracts: Shenoy, K. V., Crowell, J. A., Bradley, D. C., Andersen, R. A. 1997; 23
  • Monolithic integration of SEEDs and VLSI GaAs circuits by epitaxy on electronics. EEE Photon. Technol. Lett. Wang, H., Luo, J., Shenoy, K. V., Fonstad, C. G., Psaltis, D. 1997; 9: 607-609.
  • Self-motion path perception during head and body rotations. IOVS/ARVO Crowell, J. A., Banks, M. S., Shenoy, K. V., Andersen, R. A. 1997; 38
  • Mechanisms of heading perception in primate visual cortex SCIENCE BRADLEY, D. C., Maxwell, M., Andersen, R. A., Banks, M. S., Shenoy, K. V. 1996; 273 (5281): 1544-1547

    Abstract

    When we move forward while walking or driving, what we see appears to expand. The center or focus of this expansion tells us our direction of self-motion, or heading, as long as our eyes are still. However, if our eyes move, as when tracking a nearby object on the ground, the retinal image is disrupted and the focus is shifted away from the heading. Neurons in primate dorso-medial superior temporal area responded selectively to an expansion focus in a certain part of the visual field, and this selective region shifted during tracking eye movements in a way that compensated for the retinal focus shift. Therefore, these neurons account for the effect of eye movements on what we see as we travel forward through the world.

    View details for Web of Science ID A1996VG59700039

    View details for PubMedID 8703215

  • Neural mechanisms for heading and structure-from-motion perception COLD SPRING HARBOR SYMPOSIA ON QUANTITATIVE BIOLOGY Andersen, R. A., BRADLEY, D. C., Shenoy, K. V. 1996; 61: 15-25

    View details for Web of Science ID A1996XD58000004

    View details for PubMedID 9246431

  • Heading computation during pursuit eye movements in cortical area MSTd. Soc. for Neurosci. Abstracts: Bradley, D. C., Maxwell, M., Andersen, R. A., Banks, M. S., Shenoy, K. V. 1996; 22
  • Neural mechanisms for heading perception in primate visual cortex. Science Bradley, D. C., Maxwell, M., Andersen, R. A., Banks, M. S., Shenoy, K. V. 1996; 273: 1544-1547.
  • Heading computation during head movements in macaque cortical area MSTd. Soc. for Neurosci. Abstracts: Shenoy, K. V., Bradley, D. C., Andersen, R. A. 1996; 22
  • Neuroscience: Researchers find neurons that may help us navigate. Science Barinaga, M. 1996; 273: 1489-1490.
  • Elevated temperature stability of GaAs digital integrated circuits. IEEE Electron Device Lett. Braun, E. K., Shenoy, K. V., Fonstad, C. G., Mikkelson, J. M. 1996; 17: 37-39.
  • Monolithic optoelectronic circuit design and fabrication by epitaxial growth on commercial VLSI GaAs MESFETs. IEEE Photon. Technol. Lett. Shenoy, K. V., Fonstad, C. G., Grot, A. C., Psaltis, D. 1995; 7: 508-510.
  • A technology for monolithic integration of high indium-fraction resonant tunneling diodes with commercial MESFET VLSI electronics. InP and Related Compounds. Aggarwal, R. J., Shenoy, K. V., Fonstad, C. G. 1995
  • Monolithic optoelectronic VLSI design and fabrication for optical interconnects. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Ph.D. Thesis. Shenoy, K. V. 1995
  • Computation by symmetry operations in a highly structured model of the brain. Phys. Rev. E McGrann, J. V., Shaw, G. L., Shenoy, K. V., Matthews, R. B. 1994; 49: 5830-5839.
  • Integration of LEDs and GaAs circuits by MBE regrowth. IEEE Photon. Technol. Lett. Grot, A. C., Psaltis, D., Shenoy, K. V., Fonstad, C. G. 1994; 6: 819-821.
  • Large scale integration of LEDs and GaAs circuits fabricated through MOSIS. ICO/OSA/SPIE/ LEOS International Conf. On Optical Computing. Grot, A. C., Psaltis, D., Shenoy, K. V., Fonstad, C. G. 1994
  • High temperature stability of refractory-metal VLSI GaAs MESFETs. IEEE Electron Device Lett. Shenoy, K. V., Fonstad, C. G., Mikkelson, J. M. 1994; 15: 106-108.
  • Learning by selection in the Trion model of cortical organization. Cerebral Cortex Shenoy, K. V., Kaufman, J., McGrann, J. V., Shaw, G. L. 1993; 3: 239-248.

Books and Book Chapters


  • Neural Prosthetics In Encyclopedia of Motor Control Shenoy, K. V., Chestek, C. A. edited by Wolpert, D. Scholarpedia.. 2012: 1
  • A brain-machine interface operating with a real-time spiking neural network control algorithm. Advances in Neural Information Processing Systems (NIPS) Dethier, J., Nuyujukian, P., Elassaad, S., Stewart, T., Eliasmith, C., Shenoy, K. V. edited by Shawe-Taylor, J., Zemel, R., S., Bartlett, P. MIT Press Cambridge, MA.. 2011: 1
  • A dynamical systems view of motor preparation: Implications for neural prosthetic system design.Chapter 3 in Andrea Shenoy, K. V., Kaufman, M. T., Sahani, M., Churchland, M. M. edited by Green, M., Chapman, C., Elaine, Kalaska, F., John Amsterdam: The Netherlands.. 2011: 33-58
  • Dynamical segmentation of single trials from population neural data. Advances in Neural Information Processing Systems (NIPS) Petreska, B., Yu, B. M., Cunningham, J. P., Santhanam, G., Ryu, S. I., Shenoy, K. V. edited by Shawe-Taylor, J., Zemel, R., S., Bartlett, P. MIT Press. 2011: 1
  • Empirical models of spiking in neural populations. Advances in Neural Information Processing Systems (NIPS) Macke, J., Buesing, L., Cunningham, J. P., Yu, B. M., Shenoy, K. V., Sahani, M. edited by Shawe-Taylor, J., Zemel, R., S., Bartlett, P. MIT Press Cambridge, MA.. 2011: 1
  • Neural decoding for motor and communication prostheses. Chapter in Statistical Signal Processing for Neuroscience Yu, B. M., Santhanam, G., Sahani, M., Shenoy, K. V. edited by Elsevier. Elsevier. 2010: 219-263.
  • Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. Advances in Neural Information Processing Systems (NIPS Yu, B. M., Cunningham, J. P., Ryu, S. I., Shenoy, K. V., Sahani, M. MIT Press. 2008: 1
  • Inferring neural firing rates from spike trains using Gaussian processes. Advances in Neural Information Processing Systems (NIPS) Cunningham, J., Yu, B. M., Shenoy, K. V., ahani, M. edited by J, P., D, K., Y, S. MIT Press Cambridge, MA.. 2008: 1
  • Extracting dynamical structure embedded in neural activity. Neural Information Processing Systems (NIPS) Yu, B. M., Afshar, A., Santhanam, G., Ryu, S. I., Shenoy, K. V., Sahani, M. edited by Y, W., B, S., J, P. MIT Press, Cambridge, MA.. 2006: 1545-1552.
  • Reconfigurable Neural-Prosthetics Processors. Toward Replacement Parts for the Brain Implantable Biomimetic Electronics as Neural Prostheses Mumbru, J., Shenoy, K. V., Panotopoulos, G., Ay, S., An, X., Mok, F. edited by Berger, T., Glanzman, D. MIT Press, Cambridge, MA.. 2005: 335-368.
  • Neural mechanisms for self-motion perception in area MST. International Review of Neurobiology Andersen, R. A., Shenoy, K. V., Crowell, J. A., Bradley, D. C. Academic Press.. 2000: 219-233.
  • Learning and memory processes and the modularity of the brain. Neural Bases of Learning and Memory Leng, X., McGrann, J. V., Quillfeldt, J. A., Shaw, G. L., Shenoy, K. V. edited by Delacour, J. World Scientific Press.. 1994: 1

Conference Proceedings


  • Long-term stability of neural prosthetic control signals from silicon cortical arrays in rhesus macaque motor cortex Chestek, C. A., Gilja, V., Nuyujukian, P., Foster, J. D., Fan, J. M., Kaufman, M. T., Churchland, M. M., Rivera-Alvidrez, Z., Cunningham, J. P., Ryu, S. I., Shenoy, K. V. IOP PUBLISHING LTD. 2011

    Abstract

    Cortically-controlled prosthetic systems aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic devices. Recent reports have demonstrated reasonably high levels of performance and control of computer cursors and prosthetic limbs, but to achieve true clinical viability, the long-term operation of these systems must be better understood. In particular, the quality and stability of the electrically-recorded neural signals require further characterization. Here, we quantify action potential changes and offline neural decoder performance over 382 days of recording from four intracortical arrays in three animals. Action potential amplitude decreased by 2.4% per month on average over the course of 9.4, 10.4, and 31.7 months in three animals. During most time periods, decoder performance was not well correlated with action potential amplitude (p > 0.05 for three of four arrays). In two arrays from one animal, action potential amplitude declined by an average of 37% over the first 2 months after implant. However, when using simple threshold-crossing events rather than well-isolated action potentials, no corresponding performance loss was observed during this time using an offline decoder. One of these arrays was effectively used for online prosthetic experiments over the following year. Substantial short-term variations in waveforms were quantified using a wireless system for contiguous recording in one animal, and compared within and between days for all three animals. Overall, this study suggests that action potential amplitude declines more slowly than previously supposed, and performance can be maintained over the course of multiple years when decoding from threshold-crossing events rather than isolated action potentials. This suggests that neural prosthetic systems may provide high performance over multiple years in human clinical trials.

    View details for DOI 10.1088/1741-2560/8/4/045005

    View details for Web of Science ID 000292962800009

    View details for PubMedID 21775782

  • Toward Clinically Viable Brain-Machine Interfaces Shenoy, K. V. ELSEVIER SCIENCE INC. 2011: 193S-193S
  • Monkey Models for Brain-Machine Interfaces: The Need for Maintaining Diversity Nuyujukian, P., Fan, J. M., Gilja, V., Kalanithi, P. S., Chestek, C. A., Shenoy, K. V. IEEE. 2011: 1301-1305

    Abstract

    Brain-machine interfaces (BMIs) aim to help disabled patients by translating neural signals from the brain into control signals for guiding prosthetic arms, computer cursors, and other assistive devices. Animal models are central to the development of these systems and have helped enable the successful translation of the first generation of BMIs. As we move toward next-generation systems, we face the question of which animal models will aid broader patient populations and achieve even higher performance, robustness, and functionality. We review here four general types of rhesus monkey models employed in BMI research, and describe two additional, complementary models. Given the physiological diversity of neurological injury and disease, we suggest a need to maintain the current diversity of animal models and to explore additional alternatives, as each mimic different aspects of injury or disease.

    View details for Web of Science ID 000298810001110

    View details for PubMedID 22254555

  • A 96-channel full data rate direct neural interface in 0.13 um CMOS. Walker, R. M., Gao, H., Nuyujukian, P., Makinwa, K., Shenoy, K. V., Meng, T. H. 2011
  • Low-Dimensional Neural Features Predict Muscle EMG Signals Rivera-Alvidrez, Z., Kalmar, R. S., Ryu, S. I., Shenoy, K. V. IEEE. 2010: 6027-6033

    Abstract

    Understanding the relationship between neural activity in motor cortex and muscle activity during movements is important both for basic science and for the design of neural prostheses. While there has been significant work in decoding muscle EMG from neural data, decoders often require many parameters which make the analysis susceptible to overfitting, which reduces generalizability and makes the results difficult to interpret. To address this issue, we recorded simultaneous neural activity from the motor cortices (M1/PMd) of rhesus monkeys performing an arm-reaching task while recording EMG from arm muscles. In this work, we focused on relating the mean neural activity (averaged across reach trials to one target) to the corresponding mean EMG. We reduced the dimensionality of the neural data and found that the curvature of the low-dimensional (low-D) neural activity could be used as a signature of muscle activity. Using this signature, and without directly fitting EMG data to the neural activity, we derived neural axes based on reaches to only one reach target (< 5% of the data) that could explain EMG for reaches across multiple targets (average R(2) = 0.65). Our results suggest that cortical population activity is tightly related to muscle EMG measurements, predicting a lag between cortical activity and movement generation of 47.5 ms. Furthermore, our ability to predict EMG features across different movements suggests that there are fundamental axes or directions in the low-D neural space along which the neural population activity moves to activate particular muscles.

    View details for Web of Science ID 000287964006107

    View details for PubMedID 21097116

  • A high-performance cortically-controlled motor prosthesis enabled by a feedback control perspective. Gilja, V., Nuyujukian, P., Chestek, C. A., Cunningham, J. P., Yu, B. M., Ryu, S. I., Shenoy, Krishna, V. 2010
  • An online, closed-loop testing platform for neural prosthetic systems. Cunningham, J. P., Nuyujukian, P., Gilja, V., Chestek, C. A., Ryu, S. I., Shenoy, K. V. 2010
  • Waveform stability and neural decoder performance across 7 weeks. Chestek, C. A., Gilja, V., Nuyujukian, P., Foster, J. D., Kaufman, M. T., Ryu, S. I., Shenoy, Krishna, V. 2010
  • Low dimensional neural features predict specific muscle EMG signals. Rivera-Alvidrez, Z., Kalmar, R., Ryu, S. I., Shenoy, K. V. 2010
  • Neural Prosthetic Systems: Current Problems and Future Directions Chestek, C. A., Cunningham, J. P., Gilja, V., Nuyujukian, P., Ryu, S. I., Shenoy, K. V. IEEE. 2009: 3369-3375

    Abstract

    By decoding neural activity into useful behavioral commands, neural prosthetic systems seek to improve the lives of severely disabled human patients. Motor decoding algorithms, which map neural spiking data to control parameters of a device such as a prosthetic arm, have received particular attention in the literature. Here, we highlight several outstanding problems that exist in most current approaches to decode algorithm design. These include two problems that we argue will unlikely result in further dramatic increases in performance, specifically spike sorting and spiking models. We also discuss three issues that have been less examined in the literature, and we argue that addressing these issues may result in dramatic future increases in performance. These include: non-stationarity of recorded waveforms, limitations of a linear mappings between neural activity and movement kinematics, and the low signal to noise ratio of the neural data. We demonstrate these problems with data from 39 experimental sessions with a non-human primate performing reaches and with recent literature. In all, this study suggests that research in cortically-controlled prosthetic systems may require reprioritization to achieve performance that is acceptable for a clinically viable human system.

    View details for Web of Science ID 000280543602209

    View details for PubMedID 19963796

  • Human cortical prostheses: Lost in translation? Neurosurgical Focus Ryu, S. I., Shenoy, K. V. edited by guest, P. P. 2009
  • Single-trial representation of uncertainty about reach goals in macaque PMd. Rivera, Z. A., Kalmar, R., Afshar, A., Santhanam, G., Yu, B. M., Ryu, S. I., Shenoy, Krishna, V. 2008
  • Fast gaussian process methods for point process intensity estimation Cunningham, J. P., Sahani, M., Shenoy, K. V. 2008
  • Neural basis of reach preparation. Shenoy, K. V. 2008
  • HermesC: RF wireless low-power neural recording system for freely behaving primates. Gilja, V., Chestek, C., Nuyujukian, P., Ryu, S. I., Kier, R., Solzbacher, F., Shenoy, Krishna, V. 2008
  • Inferring neural firing rates from spike trains using Gaussian processes. Cunningham, J., Yu, B. M., Sahani, M., Shenoy, K. V. 2007
  • The timecourse of neural variability in visual area MT. Churchland, M. M., Bradley, D. C., Clark, A., Hosseini, P., Cohen, M. R., Newsome, W. T., Shenoy, Krishna, V. 2007
  • Optimizing spike sorting for brain computer interfaces with non-stationary waveforms. Gilja, V., Santhanam, G., Linderman, M., Afshar, A., Ryu, S. I., Meng, T. H., Shenoy, Krishna, V. 2007
  • Single-trial representation of uncertainty about reach goals in macaque PMd. Rivera, Z., Kalmar, R., Afshar, A., Santhanam, G., Yu, B. M., Ryu, S. I., Shenoy, Krishna, V. 2007
  • Potential role of neural preparatory activity in optimal control theory Shenoy, K. V., Churchland, M. M. 2007
  • Extracting dynamical structure embedded in premotor cortical activity. Shenoy, K. V., Yu, B. M., Afshar, A., Churchland, M. M., Cunningham, J. P., Sahani, M. 2007
  • Neural correlates of movement preparation. Churchland, M. M., Shenoy, K. V. 2007
  • The activity of motor cortex neurons during reaches is temporally complex and exceedingly heterogeneous. Churchland, M. M., Shenoy, K. V. 2006
  • Modulation of neuronal ensemble activity during movement planning in Parkinson's disease patients undergoing deep brain stimulation. Henderson, J. M., Afshar, A., Ryu, S. I., Hill, B. C., Bronte-Stewart, H. M., Shenoy, K. V. 2006
  • Heterogeneous reference frames for reaching in macaque PMd. Batista, A. P., Santhanam, G., Yu, B., Ryu, S. I., Afshar, A., Shenoy, K. V. 2006
  • Acute implantation of high density microelectrode arrays for investigation of human cortex. Henderson, J. M., Afshar, A., Ryu, S. I., Hill, B. C., Bronte-Stewart, H. M., Shenoy, K. V. 2006
  • Integrated optical sensors for chronic, minimally-invasive imaging of brain function. Lee, T. T., O, L., Cang, J., Kaneko, M., Stryker, M. P., Smith, S. J., Shenoy, Krishna, V. 2006
  • Extracting dynamical structure embedded in motor preparatory activity. Yu, B. M., Afshar, A., Shenoy, K. V., Sahani, M. 2005
  • Feedback-directed state transition for recursive Bayesian estimation of goal-directed trajectories. Yu, B. M., Santhanam, G., Ryu, S. I., Shenoy, K. V. 2005
  • Neural variability in premotor cortex provides a signature of motor preparation. Churchland, M. M., Yu, B. M., Ryu, S., Santhanam, G., Shenoy, K. V. 2005
  • Behavioral variability predicted from recorded plan activity. Churchland, M. M., Shenoy, K. 2004
  • Settling recurrent networks underlie motor planning in the primate brain. Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G., Shenoy, K. V. 2004
  • The speed at which reach movement plans can be decoded from the cortex and its implications for high performance neural prosthetic arm systems. Ryu, S. I., Santhanam, G., Yu, B. M., Shenoy, K. V. 2004
  • Role of movement preparation in movement generation. Churchland, M. M., Yu, B. M., Ryu, S. I., Santhanam, G., Afshar, A., Shenoy, K. V. 2004
  • Contribution of motor preparation and execution noise to goal-irrelevant movement variability. Afshar, A., Churchland, M. M., Shenoy, K. V. 2004
  • Reconstruction of arm trajectories from plan and peri-movement motor cortical activity. Kemere, C., Santhanam, G., Ryu, S. I., Yu, B. M., Meng, T. H., Shenoy, K. V. 2004
  • Improving neural prosthetic system performance for a fixed number of neurons. Yu, B. M., Ryu, S., Churchland, M. M., Shenoy, K. V. 2004
  • Premotor cortex plan activity used to decode upcoming reach speed for high-performance neural prosthetic system design. Ryu, S. I., Yu, B. M., Churchland, M. M., Shenoy, K. V. 2004
  • Comparison of Si/CMOS and GaAs MESFET technologies for analog optoelectronic circuits. Grot, A. C., Psaltis, D., Shenoy, K. V., Fonstad, C. G. 1994
  • Application specific OEICs fabricated using GaAs IC foundry services. Fonstad Jr., C, G, Shenoy, K. V. 1994
  • GaAs optoelectronic winner-take-all circuit. Grot, A. C., Psaltis, D., Shenoy, K. V., Fonstad, C. G. 1994
  • Lowered temperature MBE regrowth of LED structures on high density GaAs circuits fabricated through MOSIS. Grot, A. C., Psaltis, D., Shenoy, K. V., Fonstad, C. G. 1993
  • Optoelectronic VLSI circuit fabrication. Shenoy, K. V., Nuytkens, P., Fonstad, C. G., Johnson, G. D., Goodhue, W. D., Donnelly, J. 1993
  • MBE regrowth of LEDs on VLSI GaAs MESFETs. Shenoy, K. V., Fonstad, C. G., Grot, A. C., Psaltis, D. 1993
  • GaAs optoelectronic neuron circuits fabricated through MOSIS. Grot, A. C., Psaltis, D., Shenoy, K. V., Fonstad, C. G. 1993
  • Laser diodes and refractory-metal gate VLSI GaAs MESFETs for smart pixels. Shenoy, K. V., Fonstad, C. G., Elman, B., Crawford, F. D., Mikkelson, J. M. 1992
  • Selectional learning in the Trion model of cortical organization. Shenoy, K. V., Kaufman, J., McGrann, J., Shaw, G. L. 1989
  • Rotational invariance in the Trion model of cortical organization. McGrann, J. V., Shenoy, K. V., Shaw, G. 1989

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