Assistant Professor, Department of Biology
Ph.D. Columbia University
B.Sc. Sharif University of Technology, Tehran, Iran
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?
The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability.
Neuron. 2018 May 16;98(4):846-860.e5
Authors: Hennequin G, Ahmadian Y, Rubin DB, Lengyel M, Miller KD
Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states ("attractors") or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic "stabilized supralinear network"), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception.
PMID: 29772203 [PubMed - in process]