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

Contact

  • Academic
    khaledi@stanford.edu
    University - Scholar Department: Neurosurgery Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5327

Links

Current Research and Scholarly Interests

Computational and theoretical neuroscience

Biological physics

Stochastic processes

All Publications

  • Impact of number of stimulation sites on long-lasting desynchronization effects of coordinated reset stimulation CHAOS Kromer, J. A., Khaledi-Nasab, A., Tass, P. A. 2020; 30

    View details for DOI 10.1063/5.0015196

  • Collective Dynamics of Excitable Tree Networks Khaledi Nasab, A. Ohio University . Athens, Ohio. 2019 178

    Abstract

    We study the collective dynamics of diffusively coupled excitable elements in small tree networks with regular and random connectivity, which model sensory neurons with branched myelinated distal terminals. These neurons possess dendritic trees with myelinated branches and with nodes of Ranvier at every branching points. They may show spontaneous noisy periodic spiking. Examples of such neurons include touch receptors, muscle spindles afferents and some electroreceptors. A mathematical model of such a neuron is a system of excitable elements coupled on a tree network. We show that the mechanism of periodic firing is rooted in the synchronization of local activity of individual nodes, even though peripheral nodes may receive random independent inputs. We developed a theory that predicts the collective spiking activity in physiologically-relevant strong coupling limit. The structural variability in random tree networks translates into collective network dynamics leading to a wide range of firing rates and coefficients of variations, which is most pronounced in the strong coupling regime. We studied signal detection in regular and random trees. Our results indicate that the highest sensitivity occurs in specific optimum values of the input current for any given tree network. In the presence of a time-dependent uniform stimulus, we have shown that the highest information carried by spikes of the central node of a tree about the stimulus is attained for the strong coupling, even though the firing rate is at maximum for smaller values of coupling strength. Finally, we studied the effect of inhomogeneous inputs on the collective response of tree networks and showed that it leads to additional variability of collective firing.

    PhD thesis
  • 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)

    View details for DOI 10.1103/PhysRevE.98.052303

    View details for Web of Science ID 000449910000004

  • 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