Open AccessNonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance
Author(s) -
Cody Baker,
Vicky Zhu,
Robert Rosenbaum
Publication year - 2020
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1008192
Subject(s) - excitatory postsynaptic potential , stimulus (psychology) , nonlinear system , neuroscience , computation , inhibitory postsynaptic potential , biological neural network , artificial neural network , models of neural computation , computer science , population , psychology , artificial intelligence , algorithm , physics , cognitive psychology , quantum mechanics , demography , sociology
Balanced excitation and inhibition is widely observed in cortex. How does this balance shape neural computations and stimulus representations? This question is often studied using computational models of neuronal networks in a dynamically balanced state. But balanced network models predict a linear relationship between stimuli and population responses. So how do cortical circuits implement nonlinear representations and computations? We show that every balanced network architecture admits stimuli that break the balanced state and these breaks in balance push the network into a “semi-balanced state” characterized by excess inhibition to some neurons, but an absence of excess excitation. The semi-balanced state produces nonlinear stimulus representations and nonlinear computations, is unavoidable in networks driven by multiple stimuli, is consistent with cortical recordings, and has a direct mathematical relationship to artificial neural networks.