Bio

Bio


Ali is a postdoctoral fellow, working in the fields of stimulation-induced modulation of structural plasticity, propagation of desynchronizing effects, and control of stimulation with machine learning. Trained as a theoretical and computational physicist, Ali has expertise in the fields of computational neuroscience, nonlinear dynamics, stochastic processes, and network sciences. For his PhD in Physics, Ali worked with Prof. Alexander Neiman at Ohio University, where he studied the collective dynamics of excitable tree networks, which is relevant to some sensory neurons such as gentle touch receptors, muscle spindles, and some electroreceptors. Ali's goal is to use his skills to develop brain stimulation techniques for the treatment of neurological disorders such as Parkinson's disease.

Honors & Awards


  • Condensed Matter and Surface Science (CMSS) Fellowship, Ohio University (2019)
  • Shirly Chen Student Award, American Physical Society (2019)

Boards, Advisory Committees, Professional Organizations


  • Member, American Physical Society (APS) (2012 - Present)

Professional Education


  • PhD, Ohio University, Physics (Biophysics) (2019)
  • MSc, Ohio University, Physics (Biophysics) (2016)
  • MSc, University of Bonab, Physics (2014)
  • BSc, Shahid Chamran University, Physics (2011)

Stanford Advisors


Research & Scholarship

Current Research and Scholarly Interests


Computational and theoretical neuroscience

Biological physics

Stochastic processes

Publications

All Publications


  • Variability of collective dynamics in random tree networks of strongly coupled stochastic excitable elements PHYSICAL REVIEW E Khaledi-Nasab, A., Kromer, J. A., Schimansky-Geier, L., Neiman, A. B. 2018; 98 (5)
  • Emergent stochastic oscillations and signal detection in tree networks of excitable elements SCIENTIFIC REPORTS Kromer, J., Khaledi-Nasab, A., Schimansky-Geier, L., Neiman, A. B. 2017; 7: 3956

    Abstract

    We study the stochastic dynamics of strongly-coupled excitable elements on a tree network. The peripheral nodes receive independent random inputs which may induce large spiking events propagating through the branches of the tree and leading to global coherent oscillations in the network. This scenario may be relevant to action potential generation in certain sensory neurons, which possess myelinated distal dendritic tree-like arbors with excitable nodes of Ranvier at peripheral and branching nodes and exhibit noisy periodic sequences of action potentials. We focus on the spiking statistics of the central node, which fires in response to a noisy input at peripheral nodes. We show that, in the strong coupling regime, relevant to myelinated dendritic trees, the spike train statistics can be predicted from an isolated excitable element with rescaled parameters according to the network topology. Furthermore, we show that by varying the network topology the spike train statistics of the central node can be tuned to have a certain firing rate and variability, or to allow for an optimal discrimination of inputs applied at the peripheral nodes.

    View details for DOI 10.1038/s41598-017-04193-8

    View details for Web of Science ID 000403840000028

    View details for PubMedID 28638071

    View details for PubMedCentralID PMC5479816

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