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)
PhD, Ohio University, Physics (Biophysics) (2019)
MSc, Ohio University, Physics (Biophysics) (2016)
MSc, University of Bonab, Physics (2014)
BSc, Shahid Chamran University, Physics (2011)
Peter Tass, Postdoctoral Faculty Sponsor
Current Research and Scholarly Interests
Computational and theoretical neuroscience
Information processing in tree networks of excitable elements.
Physical review. E
2021; 103 (1-1): 012308
We study the collective response of small random tree networks of diffusively coupled excitable elements to stimuli applied to leaf nodes. Such networks model the morphology of certain sensory neurons that possess branched myelinated dendrites with excitable nodes of Ranvier at every branch point and at leaf nodes. Leaf nodes receive random inputs along with a stimulus and initiate action potentials that propagate through the tree. We quantify the collective response registered at the central node using mutual information. We show that in the strong-coupling limit, the statistics of the number of nodes and leaves determines the mutual information. At the same time, the collective response is insensitive to particular node connectivity and distribution of stimulus over leaf nodes. However, for intermediate coupling, the mutual information may strongly depend on the stimulus distribution among leaf nodes. We identify a mechanism behind the competition of leaf nodes that leads to nonmonotonous dependence of mutual information on coupling strength. We show that a localized stimulus given to a tree branch can be occluded by the background firing of unstimulated branches, thus suppressing mutual information. Nonetheless, the mutual information can be enhanced by a proper stimulus localization and tuning of coupling strength.
View details for DOI 10.1103/PhysRevE.103.012308
View details for PubMedID 33601542
Long-Lasting Desynchronization of Plastic Neural Networks by Random Reset Stimulation
Frontiers in physiology
View details for DOI 10.3389/fphys.2020.622620
Impact of number of stimulation sites on long-lasting desynchronization effects of coordinated reset stimulation
View details for DOI 10.1063/5.0015196
Long-Lasting Desynchronization of Plastic Neural Networks by Random Reset Stimulation.
Frontiers in physiology
2020; 11: 622620
Excessive neuronal synchrony is a hallmark of neurological disorders such as epilepsy and Parkinson's disease. An established treatment for medically refractory Parkinson's disease is high-frequency (HF) deep brain stimulation (DBS). However, symptoms return shortly after cessation of HF-DBS. Recently developed decoupling stimulation approaches, such as Random Reset (RR) stimulation, specifically target pathological connections to achieve long-lasting desynchronization. During RR stimulation, a temporally and spatially randomized stimulus pattern is administered. However, spatial randomization, as presented so far, may be difficult to realize in a DBS-like setup due to insufficient spatial resolution. Motivated by recently developed segmented DBS electrodes with multiple stimulation sites, we present a RR stimulation protocol that copes with the limited spatial resolution of currently available depth electrodes for DBS. Specifically, spatial randomization is realized by delivering stimuli simultaneously to L randomly selected stimulation sites out of a total of M stimulation sites, which will be called L/M-RR stimulation. We study decoupling by L/M-RR stimulation in networks of excitatory integrate-and-fire neurons with spike-timing dependent plasticity by means of theoretical and computational analysis. We find that L/M-RR stimulation yields parameter-robust decoupling and long-lasting desynchronization. Furthermore, our theory reveals that strong high-frequency stimulation is not suitable for inducing long-lasting desynchronization effects. As a consequence, low and high frequency L/M-RR stimulation affect synaptic weights in qualitatively different ways. Our simulations confirm these predictions and show that qualitative differences between low and high frequency L/M-RR stimulation are present across a wide range of stimulation parameters, rendering stimulation with intermediate frequencies most efficient. Remarkably, we find that L/M-RR stimulation does not rely on a high spatial resolution, characterized by the density of stimulation sites in a target area, corresponding to a large M. In fact, L/M-RR stimulation with low resolution performs even better at low stimulation amplitudes. Our results provide computational evidence that L/M-RR stimulation may present a way to exploit modern segmented lead electrodes for long-lasting therapeutic effects.
View details for DOI 10.3389/fphys.2020.622620
View details for PubMedID 33613303
View details for PubMedCentralID PMC7893102
Collective Dynamics of Excitable Tree Networks
Ohio University .
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 2018; 98 (5)
Emergent stochastic oscillations and signal detection in tree networks of excitable elements
2017; 7: 3956
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