Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity
Name
2107.04084.pdf
Description
Accepted version
Size
1.05 MB
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Unknown
Checksum (MD5)
7edfd6b4e687ee6efe9d674621ee3cfd
Author(s)
Jazayeri, Mehrdad
Ostojic, Srdjan
Date Issued
October 2021
Journal
Current Opinion in Neurobiology
Publisher
Elsevier BV
Citation
Jazayeri, Mehrdad and Ostojic, Srdjan. 2021. "Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity." Current Opinion in Neurobiology, 70.
Version
Author's final manuscript
Abstract
The ongoing exponential rise in recording capacity calls for new approaches for analysing and interpreting neural data. Effective dimensionality has emerged as an important property of neural activity across populations of neurons, yet different studies rely on different definitions and interpretations of this quantity. Here, we focus on intrinsic and embedding dimensionality, and discuss how they might reveal computational principles from data. Reviewing recent works, we propose that the intrinsic dimensionality reflects information about the latent variables encoded in collective activity while embedding dimensionality reveals the manner in which this information is processed. We conclude by highlighting the role of network models as an ideal substrate for testing more specifically various hypotheses on the computational principles reflected through intrinsic and embedding dimensionality.
MIT Department
McGovern Institute for Brain Research at MIT
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.conb.2021.08.002
