Contextual modulation in mammalian neocortex is asymmetric

Kay, J. W. and Phillips, W. A. (2020) Contextual modulation in mammalian neocortex is asymmetric. Symmetry, 12(5), 815. (doi: 10.3390/sym12050815)

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Abstract

Neural systems are composed of many local processors that generate an output given their many inputs as specified by a transfer function. This paper studies a transfer function that is fundamentally asymmetric and builds on multi-site intracellular recordings indicating that some neocortical pyramidal cells can function as context-sensitive two-point processors in which some inputs modulate the strength with which they transmit information about other inputs. Learning and processing at the level of the local processor can then be guided by the context of activity in the system as a whole without corrupting the message that the local processor transmits. We use a recent advance in the foundations of information theory to compare the properties of this modulatory transfer function with that of the simple arithmetic operators. This advance enables the information transmitted by processors with two distinct inputs to be decomposed into those components unique to each input, that shared between the two inputs, and that which depends on both though it is in neither, i.e., synergy. We show that contextual modulation is fundamentally asymmetric, contrasts with all four simple arithmetic operators, can take various forms, and can occur together with the anatomical asymmetry that defines pyramidal neurons in mammalian neocortex.

Item Type:Articles
Keywords:Asymmetry, multivariate mutual information, information decomposition, contextual modulation, synergy, neural systems.
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Kay, Dr James
Creator Roles:
Kay, J.Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing
Authors: Kay, J. W., and Phillips, W. A.
College/School:College of Science and Engineering > School of Mathematics and Statistics > Statistics
Journal Name:Symmetry
Publisher:MDPI
ISSN:2073-8994
ISSN (Online):2073-8994
Published Online:14 May 2020
Copyright Holders:Copyright © 2020 The Authors
First Published:First published in Symmetry 12(5): 815
Publisher Policy:Reproduced under a Creative Commons License

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