| Literature DB >> 32640928 |
Saurabh Vyas1,2, Matthew D Golub3,2, David Sussillo3,2,4, Krishna V Shenoy1,3,2,5.
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.Entities:
Keywords: dynamical systems; neural computation; neural population dynamics; state spaces
Mesh:
Year: 2020 PMID: 32640928 PMCID: PMC7402639 DOI: 10.1146/annurev-neuro-092619-094115
Source DB: PubMed Journal: Annu Rev Neurosci ISSN: 0147-006X Impact factor: 12.449