Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons

• Type of Material: Article
• Authors: Audrey Sederberg, Emory University; Ilya Nemenman, Emory University
• Language: English
• Date: 2020-05-01
• Publisher: Public Library Science
• Publication Version: Final Published Version
• Copyright Statement: © 2020 Sederberg, Nemenman.
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pcbi.1007875
• Title of Journal or Parent Work: PLoS Computational Biology
• Volume: 16
• Issue: 5
• Start Page: e1007875
• End Page: e1007875
• Grant/Funding Information: During the early conception of this project, AS was supported by NIH/NINDS U01NS094302 and R01NS104928.; This work was supported by NIH Grants R01NS084844 (AS and IN), R01EB022872, and R01NS099375 (IN), and by NSF Grant BCS-1822677 (IN).
• Keywords: Orientation selectivity; Science & Technology; Life Sciences & Biomedicine; Patterns; Cortex; Mathematical & Computational Biology; Biochemistry & Molecular Biology; Inhibitory neurons; Decision making; Dynamics; Biochemical Research Methods
• Research Categories: Biology, Molecular; Computer Science; Chemistry, Biochemistry; Biology, Neuroscience

Abstract

Modern recording methods enable sampling of thousands of neurons during the performance of behavioral tasks, raising the question of how recorded activity relates to theoretical models. In the context of decision making, functional connectivity between choiceselective cortical neurons was recently reported. The straightforward interpretation of these data suggests the existence of selective pools of inhibitory and excitatory neurons. Computationally investigating an alternative mechanism for these experimental observations, we find that a randomly connected network of excitatory and inhibitory neurons generates single- cell selectivity, patterns of pairwise correlations, and the same ability of excitatory and inhibitory populations to predict choice, as in experimental observations. Further, we predict that, for this task, there are no anatomically defined subpopulations of neurons representing choice, and that choice preference of a particular neuron changes with the details of the task. We suggest that distributed stimulus selectivity and functional organization in population codes could be emergent properties of randomly connected networks.