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Computation Through Neural Population Dynamics

delete2020-07-08
delete298
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OA
AI
S
Saurabh Vyas *
M
Matthew D. Golub
D
David Sussillo
K
Krishna V. Shenoy
DOI:10.1146/annurev-neuro-092619-094115delete
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Abstract

Abstract

En 中文
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.
Keywords:
neural computation
neural population dynamics
dynamical systems
state spaces
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Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.

Journal

Annual Review of Neuroscience cover
Annual Review of Neuroscience
IF:
13.2
Papers:
930
Citations:
1.3W

Organization

S
Stanford University
Scholars:
9.6W
Papers: 8.2W
Citations: 17.0W
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