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?
Elife. 2021 May 4;10:e58523. doi: 10.7554/eLife.58523. Online ahead of print.
For many organisms, searching for relevant targets such as food or mates entails active, strategic sampling of the environment. Finding odorous targets may be the most ancient search problem that motile organisms evolved to solve. While chemosensory navigation has been well characterized in micro-organisms and invertebrates, spatial olfaction in vertebrates is poorly understood. We have established an olfactory search assay in which freely-moving mice navigate noisy concentration gradients of airborne odor. Mice solve this task using concentration gradient cues and do not require stereo olfaction for performance. During task performance, respiration and nose movement are synchronized with tens of milliseconds precision. This synchrony is present during trials and largely absent during inter-trial intervals, suggesting that sniff-synchronized nose movement is a strategic behavioral state rather than simply a constant accompaniment to fast breathing. To reveal the spatiotemporal structure of these active sensing movements, we used machine learning methods to parse motion trajectories into elementary movement motifs. Motifs fall into two clusters, which correspond to investigation and approach states. Investigation motifs lock precisely to sniffing, such that the individual motifs preferentially occur at specific phases of the sniff cycle. The allocentric structure of investigation and approach indicate an advantage to sampling both sides of the sharpest part of the odor gradient, consistent with a serial sniff strategy for gradient sensing. This work clarifies sensorimotor strategies for mouse olfactory search and guides ongoing work into the underlying neural mechanisms.