Research & Scholarship
Current Research and Scholarly Interests
Our research goal is to understand how dynamics in neuronal circuits relate and constrain the representation of information and computations upon it. We adopt three synergistic strategies: First, we analyze neural circuit population recordings to better understand the relation between neural dynamics and behavior, Second, we theoretically explore the types of dynamics that could be associated with particular network computations. Third, we analyze the structural properties of neural circuits.
Independent Studies (9)
- Directed Reading in Neurobiology
NBIO 198 (Aut, Win, Spr, Sum)
- Directed Reading in Neurobiology
NBIO 299 (Aut, Win, Spr, Sum)
- Directed Reading in Neurosciences
NEPR 299 (Aut, Win, Spr, Sum)
- Directed Studies in Applied Physics
APPPHYS 290 (Aut, Win, Spr)
- Graduate Research
NBIO 399 (Win, Spr, Sum)
- Graduate Research
NEPR 399 (Aut, Win, Spr, Sum)
- Medical Scholars Research
NBIO 370 (Win, Spr, Sum)
PHYSICS 490 (Aut, Win, Spr)
- Undergraduate Research
NBIO 199 (Aut, Win, Spr, Sum)
- Directed Reading in Neurobiology
Graduate and Fellowship Programs
An orderly single-trial organization of population dynamics in premotor cortex predicts behavioral variability.
2019; 10 (1): 216
Animals are not simple input-output machines. Their responses to even very similar stimuli are variable. A key, long-standing question in neuroscience is to understand the neural correlates of such behavioral variability. To reveal these correlates, behavior and neural population activity must be related to one another on single trials. Such analysis is challenging due to the dynamical nature of brain function (e.g., in decision making), heterogeneity across neurons and limited sampling of the relevant neural population. By analyzing population recordings from mouse frontal cortex in perceptual decision-making tasks, we show that an analysis approach tailored to the coarse grain features of the dynamics is able to reveal previously unrecognized structure in the organization of population activity. This structure is similar on error and correct trials, suggesting dynamics that may be constrained by the underlying circuitry, is able to predict multiple aspects of behavioral variability and reveals long time-scale modulation of population activity.
View details for DOI 10.1038/s41467-018-08141-6
View details for PubMedID 30644387
Active dendritic integration and mixed neocortical network representations during an adaptive sensing behavior.
Animals strategically scan the environment to form an accurate perception of their surroundings. Here we investigated the neuronal representations that mediate this behavior. Ca2+ imaging and selective optogenetic manipulation during an active sensing task reveals that layer 5 pyramidal neurons in the vibrissae cortex produce a diverse and distributed representation that is required for mice to adapt their whisking motor strategy to changing sensory cues. The optogenetic perturbation degraded single-neuron selectivity and network population encoding through a selective inhibition of active dendritic integration. Together the data indicate that active dendritic integration in pyramidal neurons produces a nonlinearly mixed network representation of joint sensorimotor parameters that is used to transform sensory information into motor commands during adaptive behavior. The prevalence of the layer 5 cortical circuit motif suggests that this is a general circuit computation.
View details for DOI 10.1038/s41593-018-0254-6
View details for PubMedID 30349100
Schaffer Collateral Inputs to CA1 Excitatory and Inhibitory Neurons Follow Different Connectivity Rules
JOURNAL OF NEUROSCIENCE
2018; 38 (22): 5140–52
Neural circuits, governed by a complex interplay between excitatory and inhibitory neurons, are the substrate for information processing, and the organization of synaptic connectivity in neural network is an important determinant of circuit function. Here, we analyzed the fine structure of connectivity in hippocampal CA1 excitatory and inhibitory neurons innervated by Schaffer collaterals (SCs) using mGRASP in male mice. Our previous study revealed spatially structured synaptic connectivity between CA3 and CA1 pyramidal cells (PCs). Surprisingly, parvalbumin-positive interneurons (PVs) showed a significantly more random pattern spatial structure. Notably, application of Peters' rule for synapse prediction by random overlap between axons and dendrites enhanced structured connectivity in PCs, but, by contrast, made the connectivity pattern in PVs more random. In addition, PCs in a deep sublayer of striatum pyramidale appeared more highly structured than PCs in superficial layers, and little or no sublayer specificity was found in PVs. Our results show that CA1 excitatory PCs and inhibitory PVs innervated by the same SC inputs follow different connectivity rules. The different organizations of fine scale structured connectivity in hippocampal excitatory and inhibitory neurons provide important insights into the development and functions of neural networks.SIGNIFICANCE STATEMENT Understanding how neural circuits generate behavior is one of the central goals of neuroscience. An important component of this endeavor is the mapping of fine-scale connection patterns that underlie, and help us infer, signal processing in the brain. Here, using our recently developed synapse detection technology (mGRASP and neuTube), we provide detailed profiles of synaptic connectivity in excitatory (CA1 pyramidal) and inhibitory (CA1 parvalbumin-positive) neurons innervated by the same presynaptic inputs (CA3 Schaffer collaterals). Our results reveal that these two types of CA1 neurons follow different connectivity patterns. Our new evidence for differently structured connectivity at a fine scale in hippocampal excitatory and inhibitory neurons provides a better understanding of hippocampal networks and will guide theoretical and experimental studies.
View details for DOI 10.1523/JNEUROSCI.0155-18.2018
View details for Web of Science ID 000435410700010
View details for PubMedID 29728449
Science (New York, N.Y.)
2017; 356 (6340): 849-853
Ring attractors are a class of recurrent networks hypothesized to underlie the representation of heading direction. Such network structures, schematized as a ring of neurons whose connectivity depends on their heading preferences, can sustain a bump-like activity pattern whose location can be updated by continuous shifts along either turn direction. We recently reported that a population of fly neurons represents the animal's heading via bump-like activity dynamics. We combined two-photon calcium imaging in head-fixed flying flies with optogenetics to overwrite the existing population representation with an artificial one, which was then maintained by the circuit with naturalistic dynamics. A network with local excitation and global inhibition enforces this unique and persistent heading representation. Ring attractor networks have long been invoked in theoretical work; our study provides physiological evidence of their existence and functional architecture.
View details for DOI 10.1126/science.aal4835
View details for PubMedID 28473639
Angular velocity integration in a fly heading circuit
Many animals maintain an internal representation of their heading as they move through their surroundings. Such a compass representation was recently discovered in a neural population in theDrosophila melanogastercentral complex, a brain region implicated in spatial navigation. Here, we use two-photon calcium imaging and electrophysiology in head-fixed walking flies to identify a different neural population that conjunctively encodes heading and angular velocity, and is excited selectively by turns in either the clockwise or counterclockwise direction. We show how these mirror-symmetric turn responses combine with the neurons' connectivity to the compass neurons to create an elegant mechanism for updating the fly's heading representation when the animal turns in darkness. This mechanism, which employs recurrent loops with an angular shift, bears a resemblance to those proposed in theoretical models for rodent head direction cells. Our results provide a striking example of structure matching function for a broadly relevant computation.
View details for DOI 10.7554/eLife.23496
View details for Web of Science ID 000401797600001
View details for PubMedID 28530551
Maintenance of persistent activity in a frontal thalamocortical loop
2017; 545 (7653): 181-?
Persistent neural activity maintains information that connects past and future events. Models of persistent activity often invoke reverberations within local cortical circuits, but long-range circuits could also contribute. Neurons in the mouse anterior lateral motor cortex (ALM) have been shown to have selective persistent activity that instructs future actions. The ALM is connected bidirectionally with parts of the thalamus, including the ventral medial and ventral anterior-lateral nuclei. We recorded spikes from the ALM and thalamus during tactile discrimination with a delayed directional response. Here we show that, similar to ALM neurons, thalamic neurons exhibited selective persistent delay activity that predicted movement direction. Unilateral photoinhibition of delay activity in the ALM or thalamus produced contralesional neglect. Photoinhibition of the thalamus caused a short-latency and near-complete collapse of ALM activity. Similarly, photoinhibition of the ALM diminished thalamic activity. Our results show that the thalamus is a circuit hub in motor preparation and suggest that persistent activity requires reciprocal excitation across multiple brain areas.
View details for DOI 10.1038/nature22324
View details for Web of Science ID 000400963800026
View details for PubMedID 28467817
Multiplicative Updates for Optimization Problems with Dynamics
IEEE COMPUTER SOC. 2017: 2025–29
View details for Web of Science ID 000442659900359
Robust neuronal dynamics in premotor cortex during motor planning
2016; 532 (7600): 459-?
Neural activity maintains representations that bridge past and future events, often over many seconds. Network models can produce persistent and ramping activity, but the positive feedback that is critical for these slow dynamics can cause sensitivity to perturbations. Here we use electrophysiology and optogenetic perturbations in the mouse premotor cortex to probe the robustness of persistent neural representations during motor planning. We show that preparatory activity is remarkably robust to large-scale unilateral silencing: detailed neural dynamics that drive specific future movements were quickly and selectively restored by the network. Selectivity did not recover after bilateral silencing of the premotor cortex. Perturbations to one hemisphere are thus corrected by information from the other hemisphere. Corpus callosum bisections demonstrated that premotor cortex hemispheres can maintain preparatory activity independently. Redundancy across selectively coupled modules, as we observed in the premotor cortex, is a hallmark of robust control systems. Network models incorporating these principles show robustness that is consistent with data.
View details for DOI 10.1038/nature17643
View details for Web of Science ID 000374815900038
View details for PubMedID 27074502
Dynamical feature extraction at the sensory periphery guides chemotaxis
Behavioral strategies employed for chemotaxis have been described across phyla, but the sensorimotor basis of this phenomenon has seldom been studied in naturalistic contexts. Here, we examine how signals experienced during free olfactory behaviors are processed by first-order olfactory sensory neurons (OSNs) of the Drosophila larva. We find that OSNs can act as differentiators that transiently normalize stimulus intensity-a property potentially derived from a combination of integral feedback and feed-forward regulation of olfactory transduction. In olfactory virtual reality experiments, we report that high activity levels of the OSN suppress turning, whereas low activity levels facilitate turning. Using a generalized linear model, we explain how peripheral encoding of olfactory stimuli modulates the probability of switching from a run to a turn. Our work clarifies the link between computations carried out at the sensory periphery and action selection underlying navigation in odor gradients.
View details for DOI 10.7554/eLife.06694
View details for Web of Science ID 000356231400001
View details for PubMedID 26077825
From a meso- to micro-scale connectome: array tomography and mGRASP
FRONTIERS IN NEUROANATOMY
Mapping mammalian synaptic connectivity has long been an important goal of neuroscience because knowing how neurons and brain areas are connected underpins an understanding of brain function. Meeting this goal requires advanced techniques with single synapse resolution and large-scale capacity, especially at multiple scales tethering the meso- and micro-scale connectome. Among several advanced LM-based connectome technologies, Array Tomography (AT) and mammalian GFP-Reconstitution Across Synaptic Partners (mGRASP) can provide relatively high-throughput mapping synaptic connectivity at multiple scales. AT- and mGRASP-assisted circuit mapping (ATing and mGRASPing), combined with techniques such as retrograde virus, brain clearing techniques, and activity indicators will help unlock the secrets of complex neural circuits. Here, we discuss these useful new tools to enable mapping of brain circuits at multiple scales, some functional implications of spatial synaptic distribution, and future challenges and directions of these endeavors.
View details for DOI 10.3389/fnana.2015.00078
View details for Web of Science ID 000356831800001
View details for PubMedID 26089781
Structured Synaptic Connectivity between Hippocampal Regions
2014; 81 (3): 629-640
The organization of synaptic connectivity within a neuronal circuit is a prime determinant of circuit function. We performed a comprehensive fine-scale circuit mapping of hippocampal regions (CA3-CA1) using the newly developed synapse labeling method, mGRASP. This mapping revealed spatially nonuniform and clustered synaptic connectivity patterns. Furthermore, synaptic clustering was enhanced between groups of neurons that shared a similar developmental/migration time window, suggesting a mechanism for establishing the spatial structure of synaptic connectivity. Such connectivity patterns are thought to effectively engage active dendritic processing and storage mechanisms, thereby potentially enhancing neuronal feature selectivity.
View details for DOI 10.1016/j.neuron.2013.11.026
View details for Web of Science ID 000330965500016
View details for PubMedID 24412418
Mapping mammalian synaptic connectivity
CELLULAR AND MOLECULAR LIFE SCIENCES
2013; 70 (24): 4747-4757
Mapping mammalian synaptic connectivity has long been an important goal of neuroscientists since it is considered crucial for explaining human perception and behavior. Yet, despite enormous efforts, the overwhelming complexity of the neural circuitry and the lack of appropriate techniques to unravel it have limited the success of efforts to map connectivity. However, recent technological advances designed to overcome the limitations of conventional methods for connectivity mapping may bring about a turning point. Here, we address the promises and pitfalls of these new mapping technologies.
View details for DOI 10.1007/s00018-013-1417-y
View details for Web of Science ID 000327095100008
View details for PubMedID 23864031
A Hierarchical Structure of Cortical Interneuron Electrical Diversity Revealed by Automated Statistical Analysis
2013; 23 (12): 2994-3006
Although the diversity of cortical interneuron electrical properties is well recognized, the number of distinct electrical types (e-types) is still a matter of debate. Recently, descriptions of interneuron variability were standardized by multiple laboratories on the basis of a subjective classification scheme as set out by the Petilla convention (Petilla Interneuron Nomenclature Group, PING). Here, we present a quantitative, statistical analysis of a database of nearly five hundred neurons manually annotated according to the PING nomenclature. For each cell, 38 features were extracted from responses to suprathreshold current stimuli and statistically analyzed to examine whether cortical interneurons subdivide into e-types. We showed that the partitioning into different e-types is indeed the major component of data variability. The analysis suggests refining the PING e-type classification to be hierarchical, whereby most variability is first captured within a coarse subpartition, and then subsequently divided into finer subpartitions. The coarse partition matches the well-known partitioning of interneurons into fast spiking and adapting cells. Finer subpartitions match the burst, continuous, and delayed subtypes. Additionally, our analysis enabled the ranking of features according to their ability to differentiate among e-types. We showed that our quantitative e-type assignment is more than 90% accurate and manages to catch several human errors.
View details for DOI 10.1093/cercor/bhs290
View details for Web of Science ID 000327431400020
View details for PubMedID 22989582
Neuronal Circuits Underlying Persistent Representations Despite Time Varying Activity
2012; 22 (22): 2095-2103
Our brains are capable of remarkably stable stimulus representations despite time-varying neural activity. For instance, during delay periods in working memory tasks, while stimuli are represented in working memory, neurons in the prefrontal cortex, thought to support the memory representation, exhibit time-varying neuronal activity. Since neuronal activity encodes the stimulus, its time-varying dynamics appears to be paradoxical and incompatible with stable network stimulus representations. Indeed, this finding raises a fundamental question: can stable representations only be encoded with stable neural activity, or, its corollary, is every change in activity a sign of change in stimulus representation?Here we explain how different time-varying representations offered by individual neurons can be woven together to form a coherent, time-invariant, representation. Motivated by two ubiquitous features of the neocortex-redundancy of neural representation and sparse intracortical connections-we derive a network architecture that resolves the apparent contradiction between representation stability and changing neural activity. Unexpectedly, this network architecture exhibits many structural properties that have been measured in cortical sensory areas. In particular, we can account for few-neuron motifs, synapse weight distribution, and the relations between neuronal functional properties and connection probability.We show that the intuition regarding network stimulus representation, typically derived from considering single neurons, may be misleading and that time-varying activity of distributed representation in cortical circuits does not necessarily imply that the network explicitly encodes time-varying properties.
View details for DOI 10.1016/j.cub.2012.08.058
View details for Web of Science ID 000311523800017
View details for PubMedID 23084992
Effective Stimuli for Constructing Reliable Neuron Models
PLOS COMPUTATIONAL BIOLOGY
2011; 7 (8)
The rich dynamical nature of neurons poses major conceptual and technical challenges for unraveling their nonlinear membrane properties. Traditionally, various current waveforms have been injected at the soma to probe neuron dynamics, but the rationale for selecting specific stimuli has never been rigorously justified. The present experimental and theoretical study proposes a novel framework, inspired by learning theory, for objectively selecting the stimuli that best unravel the neuron's dynamics. The efficacy of stimuli is assessed in terms of their ability to constrain the parameter space of biophysically detailed conductance-based models that faithfully replicate the neuron's dynamics as attested by their ability to generalize well to the neuron's response to novel experimental stimuli. We used this framework to evaluate a variety of stimuli in different types of cortical neurons, ages and animals. Despite their simplicity, a set of stimuli consisting of step and ramp current pulses outperforms synaptic-like noisy stimuli in revealing the dynamics of these neurons. The general framework that we propose paves a new way for defining, evaluating and standardizing effective electrical probing of neurons and will thus lay the foundation for a much deeper understanding of the electrical nature of these highly sophisticated and non-linear devices and of the neuronal networks that they compose.
View details for DOI 10.1371/journal.pcbi.1002133
View details for Web of Science ID 000294299700014
View details for PubMedID 21876663
Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data
2008; 99 (4-5): 371-379
Neuron models, in particular conductance-based compartmental models, often have numerous parameters that cannot be directly determined experimentally and must be constrained by an optimization procedure. A common practice in evaluating the utility of such procedures is using a previously developed model to generate surrogate data (e.g., traces of spikes following step current pulses) and then challenging the algorithm to recover the original parameters (e.g., the value of maximal ion channel conductances) that were used to generate the data. In this fashion, the success or failure of the model fitting procedure to find the original parameters can be easily determined. Here we show that some model fitting procedures that provide an excellent fit in the case of such model-to-model comparisons provide ill-balanced results when applied to experimental data. The main reason is that surrogate and experimental data test different aspects of the algorithm's function. When considering model-generated surrogate data, the algorithm is required to locate a perfect solution that is known to exist. In contrast, when considering experimental target data, there is no guarantee that a perfect solution is part of the search space. In this case, the optimization procedure must rank all imperfect approximations and ultimately select the best approximation. This aspect is not tested at all when considering surrogate data since at least one perfect solution is known to exist (the original parameters) making all approximations unnecessary. Furthermore, we demonstrate that distance functions based on extracting a set of features from the target data (such as time-to-first-spike, spike width, spike frequency, etc.)--rather than using the original data (e.g., the whole spike trace) as the target for fitting-are capable of finding imperfect solutions that are good approximations of the experimental data.
View details for DOI 10.1007/s00422-008-0269-2
View details for Web of Science ID 000260938100011
View details for PubMedID 19011925