Computation Through Neural Population Dynamics.

Journal: Annual review of neuroscience
PMID: 32640928

Abstract

Significant experimental, computational, and theoretical work has identified rich structure within the coordinated activity of interconnected neural populations. An emerging challenge now is to uncover the nature of the associated computations, how they are implemented, and what role they play in driving behavior. We term this computation through neural population dynamics. If successful, this framework will reveal general motifs of neural population activity and quantitatively describe how neural population dynamics implement computations necessary for driving goal-directed behavior. Here, we start with a mathematical primer on dynamical systems theory and analytical tools necessary to apply this perspective to experimental data. Next, we highlight some recent discoveries resulting from successful application of dynamical systems. We focus on studies spanning motor control, timing, decision-making, and working memory. Finally, we briefly discuss promising recent lines of investigation and future directions for the computation through neural population dynamics framework.

Authors

  • Saurabh Vyas
    Electrical Engineering Department, Stanford University, Stanford, CA 94305, USA; Bioengineering Department, Stanford University, Stanford, CA 94305, USA.
  • Matthew D Golub
    Department of Electrical Engineering, Stanford University, Stanford, California 94305, USA.
  • David Sussillo
    Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA.
  • Krishna V Shenoy
    1] Department of Electrical Engineering and Neurosciences Program, Stanford University, Stanford, California, USA. [2] Departments of Bioengineering and Neurobiology, Stanford Neurosciences Institute and Bio-X Program, Stanford University, Stanford, California, USA.

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