Harnessing behavioral diversity to understand neural computations for cognition

Musall, S., Urai, A. E., Sussillo, D., Churchland, A. K. (October 2019) Harnessing behavioral diversity to understand neural computations for cognition. Curr Opin Neurobiol, 58. pp. 229-238. ISSN 0959-4388

URL: https://www.ncbi.nlm.nih.gov/pubmed/31670073
DOI: 10.1016/j.conb.2019.09.011


With the increasing acquisition of large-scale neural recordings comes the challenge of inferring the computations they perform and understanding how these give rise to behavior. Here, we review emerging conceptual and technological advances that begin to address this challenge, garnering insights from both biological and artificial neural networks. We argue that neural data should be recorded during rich behavioral tasks, to model cognitive processes and estimate latent behavioral variables. Careful quantification of animal movements can also provide a more complete picture of how movements shape neural dynamics and reflect changes in brain state, such as arousal or stress. Artificial neural networks (ANNs) could serve as artificial model organisms to connect neural dynamics and rich behavioral data. ANNs have already begun to reveal how a wide range of different behaviors can be implemented, generating hypotheses about how observed neural activity might drive behavior and explaining diversity in behavioral strategies.

Item Type: Paper
Subjects: bioinformatics
organism description > animal behavior
organism description > animal behavior > perception > cognition
bioinformatics > computational biology
organs, tissues, organelles, cell types and functions > tissues types and functions > neural networks
CSHL Authors:
Communities: CSHL labs > Churchland lab
Depositing User: Matthew Dunn
Date: 25 October 2019
Date Deposited: 08 Nov 2019 16:51
Last Modified: 02 Feb 2024 19:28
PMCID: PMC6931281
Related URLs:
URI: https://repository.cshl.edu/id/eprint/38675

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