Nonlinear Convergence Boosts Information Coding in Circuits with Parallel Outputs

Overview

Journal Proc Natl Acad Sci U S A

Specialty Science

Date 2021 Feb 17

PMID 33593894

Citations 3

Authors

Gabrielle J Gutierrez

Fred Rieke

Eric T Shea-Brown

Affiliations

Soon will be listed here.

Abstract

Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding. Our approach is to compare model circuits with different combinations of convergence, divergence, and nonlinear neurons to discover how interactions between these components affect coding efficiency. We find that a convergent circuit with divergent parallel pathways can encode more information with nonlinear subunits than with linear subunits, despite the compressive loss induced by the convergence and the nonlinearities when considered separately.

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