--- res: bibo_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.@eng' bibo_authorlist: - foaf_Person: foaf_givenName: Michael J. foaf_name: Berry, Michael J. foaf_surname: Berry - foaf_Person: foaf_givenName: Gašper foaf_name: Tkačik, Gašper foaf_surname: Tkačik foaf_workInfoHomepage: http://www.librecat.org/personId=3D494DCA-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-6699-1455 bibo_doi: 10.3389/fncom.2020.00020 bibo_volume: 14 dct_date: 2020^xs_gYear dct_identifier: - UT:000525543200001 dct_isPartOf: - http://id.crossref.org/issn/16625188 dct_language: eng dct_publisher: Frontiers@ dct_title: 'Clustering of neural activity: A design principle for population codes@' fabio_hasPubmedId: '32231528' ...