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The sloppy relationship between neural circuit structure and function
Author(s) -
Hennig Matthias H.
Publication year - 2022
Publication title -
the journal of physiology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.802
H-Index - 240
eISSN - 1469-7793
pISSN - 0022-3751
DOI - 10.1113/jp282757
Subject(s) - biological neural network , function (biology) , artificial neural network , computer science , electronic circuit , nerve net , constant (computer programming) , neuroscience , mathematics , topology (electrical circuits) , physics , artificial intelligence , psychology , biology , combinatorics , quantum mechanics , evolutionary biology , programming language
Abstract Investigating and describing the relationships between the structure of a circuit and its function has a long tradition in neuroscience. Since neural circuits acquire their structure through sophisticated developmental programmes, and memories and experiences are maintained through synaptic modification, it is to be expected that structure is closely linked to function. Recent findings challenge this hypothesis from three different angles: function does not strongly constrain circuit parameters, many parameters in neural circuits are irrelevant and contribute little to function, and circuit parameters are unstable and subject to constant random drift. At the same time, however, recent work also showed that dynamics in neural circuit activity that is related to function are robust over time and across individuals. Here this apparent contradiction is addressed by considering the properties of neural manifolds that restrict circuit activity to functionally relevant subspaces, and it will be suggested that degenerate, anisotropic and unstable parameter spaces are closely related to the structure and implementation of functionally relevant neural manifolds.