Yashar Ahmadian

Assistant Professor, Department of Biology
Member, ION

Ph.D. Columbia University
B.Sc. Sharif University of Technology, Tehran, Iran

yashar@uoregon.edu
Lab Website
Office: 238 Huestis 
Phone: 541-346-7636

 

Research Interests: Theoretical neuroscience

Overview: Our lab's research is in theoretical neuroscience. Our broad interest is in understanding how large networks of neurons, e.g. those in the mammalian cerebral cortex, process sensory inputs and give rise to higher-level cognitive functions through their collective dynamics on multiple time scales. To shed light on the complexity of neurobiological phenomena we use mathematical models that capture a few core concepts or computational and dynamical principles. We also work on developing new statistical and computational tools for analyzing large, high-dimensional neurobiological and behavioral datasets. In pursuing these goals we use techniques from statistical physics, random matrix theory, machine learning and information theory. We collaborate with experimental labs here in the University of Oregon and elsewhere.

Current questions of interest include the following. How do randomness and nonnormality in the connectivity structure of networks affect their dynamics? What roles do the horizontal and feedback connections in sensory cortical areas play in contextual modulation (how e.g. the response of neurons in the visual cortex is affected by the visual context surrounding that stimulus) and ultimately in the dynamical representation of objects? Can the breakup of neural response types in the early auditory system be explained by efficient coding principles?

RECENT PUBLICATIONS

Neuron. 2021 Aug 26:S0896-6273(21)00575-4. doi: 10.1016/j.neuron.2021.07.031. Online ahead of print.

ABSTRACT

Many studies have shown that the excitation and inhibition received by cortical neurons remain roughly balanced across many conditions. A key question for understanding the dynamical regime of cortex is the nature of this balancing. Theorists have shown that network dynamics can yield systematic cancellation of most of a neuron's excitatory input by inhibition. We review a wide range of evidence pointing to this cancellation occurring in a regime in which the balance is loose, meaning that the net input remaining after cancellation of excitation and inhibition is comparable in size with the factors that cancel, rather than tight, meaning that the net input is very small relative to the canceling factors. This choice of regime has important implications for cortical functional responses, as we describe: loose balance, but not tight balance, can yield many nonlinear population behaviors seen in sensory cortical neurons, allow the presence of correlated variability, and yield decrease of that variability with increasing external stimulus drive as observed across multiple cortical areas.

PMID:34464597 | DOI:10.1016/j.neuron.2021.07.031