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MAR 11, 2020 12:00 PM PDT

PANEL: Cracking a Neural Circuit's Function Through High-Resolution Physiology, Connectomics, and Computational Modeling

Presented at: Neuroscience 2020
C.E. Credits: P.A.C.E. CE Florida CE
Speakers
  • Associate Professor of Computational Neuroscience in the Department of Physiology and Biophysics at Weill Cornell Medicine
    Biography
      Emre Aksay is an Associate Professor of Computational Neuroscience in the Department of Physiology and Biophysics at Weill Cornell Medicine. He studied Physics at the University of Washington and Princeton University before obtaining a doctorate in Biophysics from New York University while working concurrently in the Biological Computation Division at Bell Labs. He completed his postdoctoral studies in the Department of Molecular Biology at Princeton University. Dr. Aksay's research focuses on dynamics in neural systems, with emphasis on the mechanisms underlying learning and memory. His work has been supported by the Burroughs Wellcome Fund, the Searle Scholar Program, the Frueauff Foundation, the Simons Foundation, the National Science Foundation, and the National Institutes of Health.
    • Joel Keizer Chair in Theoretical and Computational Biology at UC Davis, and Professor in the Departments of Neurobiology, Physiology, & Behavior and the Department of Ophthalmology
      Biography
        Mark Goldman is the Joel Keizer Chair in Theoretical and Computational Biology at UC Davis, where he is a Professor in the Departments of Neurobiology, Physiology, & Behavior and the Department of Ophthalmology & Vision Science. He received a PhD in Physics from Harvard University in 2000 and did his postdoctoral work in Theoretical Neuroscience at MIT. Dr. Goldman's research uses mathematical modeling and computer simulations to address the cellular, synaptic, and circuit mechanisms underlying neurobiological functions such as memory storage and motor control. His work has spanned problems ranging from cellular biophysics to neural coding, network dynamics, and biological learning rules. He serves as an action editor for the Journal of Computational Neuroscience, is a former Co-Director of the Marine Biological Laboratory's Methods in Computational Neuroscience Course, and in 2014 was appointed as an HHMI Professor.
      • Anthony B. Evnin Professor in the Neuroscience Institute and Computer Science Department at Princeton University, and Chief Research Scientist at Samsung Electronics
        Biography
          Sebastian Seung is Anthony B. Evnin Professor in the Neuroscience Institute and Computer Science Department at Princeton University, and Chief Research Scientist at Samsung Electronics. Seung has done influential research in both computer science and neuroscience. Over the past decade, he helped pioneer the new field of connectomics, applying deep learning and crowdsourcing to reconstruct neural circuits from electron microscopic images. His lab created Eyewire.org, a site that has recruited over 250,000 players from 150 countries to a game to map neural connections. His book Connectome: How the Brain's Wiring Makes Us Who We Are was chosen by the Wall Street Journal as Top Ten Nonfiction of 2012. Before joining the Princeton faculty, Seung studied at Harvard University, worked at Bell Laboratories, and taught at the Massachusetts Institute of Technology. He is an External Member of the Max Planck Society, and winner of the 2008 Ho-Am Prize in Engineering.
        • Research Associate at the Princeton Neuroscience Institute
          Biography
            Ashwin Vishwanathan is a Research Associate at Princeton University at the Princeton Neuroscience Institute. Prior to this, he was a postdoctoral associate at Princeton and at the Massachusetts Institute of Technology where he worked on developing methods to collect and image large scale electron microscopic datasets. His research focuses on studying the mechanisms that can support learning and memory. Ashwin holds a doctorate degree in Bioengineering from the University of Pittsburgh and a bachelor's in Mechanical Engineering.

          Abstract

          Mechanistic understanding of neural systems is daunting to achieve in large part due to the heterogeneity of the neuronal elements in both form and function and the complexity of the circuits formed by these elements. In this talk, we provide an update on our efforts to understand a neural system with a multi-faceted approach combining large-scale imaging of neuronal activity, reconstruction and analysis of network connectivity, and computational models of network function. We focus our work on the oculomotor neural integrator of the zebrafish, a circuit involved in the control of eye position that has been demonstrated to perform a mathematical integral of its inputs.  We identified through calcium imaging and targeted ablations neurons involved in planning, initiating, and maintaining changes in eye position. We used serial-section electron microscopy and crowd-sourced imaged analysis to reconstruct the circuit formed by these neurons and many of their synaptic partners. Computational analysis revealed a strongly-recurrent module in the circuit, consistent with theoretical predictions for the circuit mechanism of neural integration.  A whole-circuit, neural network model built from the underlying connectome reproduced the encoding of eye position seen experimentally across multiple species. We conclude with thoughts on how this approach can be extended to other domains.

          Learning Objectives:

          1. Understand how functional imaging, connectomic, and computational modeling approaches can be combined to investigate circuit mechanisms

          2. Understand the role of recurrent excitation and mutual inhibition in generating and coordinating persistent neural activity


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