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Clustering of Neural Activity: A Design Principle for Population Codes

Author(s): Berry, Michael J; Tkačik, Gašper

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Abstract: We propose that correlations among neurons are generically strong enough to organize neural activity patterns into a discrete set of clusters, which can each be viewed as a population codeword. Our reasoning starts with the analysis of retinal ganglion cell data using maximum entropy models, showing that the population is robustly in a frustrated, marginally sub-critical, or glassy, state. This leads to an argument that neural populations in many other brain areas might share this structure. Next, we use latent variable models to show that this glassy state possesses well-defined clusters of neural activity. Clusters have three appealing properties: (i) clusters exhibit error correction, i.e., they are reproducibly elicited by the same stimulus despite variability at the level of constituent neurons; (ii) clusters encode qualitatively different visual features than their constituent neurons; and (iii) clusters can be learned by downstream neural circuits in an unsupervised fashion. We hypothesize that these properties give rise to a "learnable" neural code which the cortical hierarchy uses to extract increasingly complex features without supervision or reinforcement.
Publication Date: 13-Mar-2020
Citation: Berry, Michael J, Tkačik, Gašper. (2020). Clustering of Neural Activity: A Design Principle for Population Codes.. Frontiers in computational neuroscience, 14 (20 - 20). doi:10.3389/fncom.2020.00020
DOI: doi:10.3389/fncom.2020.00020
ISSN: 1662-5188
EISSN: 1662-5188
Pages: 20 - 20
Language: eng
Type of Material: Journal Article
Journal/Proceeding Title: Frontiers in computational neuroscience
Version: Final published version. This is an open access article.

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