气候变化领域集成服务门户(CCPortal): Nonlinear convergence boosts information coding in circuits with parallel outputs

CCPortal

DOI	10.1073/pnas.1921882118
	Nonlinear convergence boosts information coding in circuits with parallel outputs
	Gutierrez G.J.; Rieke F.; Shea-Brown E.T.
发表日期	2021
ISSN	00278424
卷号	118 期号:8
英文摘要	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. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Efficient coding; Information theory; Neural computation; Retina; Sensory processing
语种	英语
scopus关键词	article; information science; nerve cell; nonlinear system; retina
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180587
作者单位	Department of Applied Mathematics, University of Washington, Seattle, WA 98195, United States; Department of Physiology and Biophysics, University of Washington, Seattle, WA 98195, United States
推荐引用方式 GB/T 7714	Gutierrez G.J.,Rieke F.,Shea-Brown E.T.. Nonlinear convergence boosts information coding in circuits with parallel outputs[J],2021,118(8).
APA	Gutierrez G.J.,Rieke F.,&Shea-Brown E.T..(2021).Nonlinear convergence boosts information coding in circuits with parallel outputs.Proceedings of the National Academy of Sciences of the United States of America,118(8).
MLA	Gutierrez G.J.,et al."Nonlinear convergence boosts information coding in circuits with parallel outputs".Proceedings of the National Academy of Sciences of the United States of America 118.8(2021).