Journal Article

Nonlinear stimulus representations in neural circuits with approximate excitatory-inhibitory balance

Zhu, Vicky, Rosenbaum, Robert, Baker, Cody

2020

Overview

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.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Animals / Artificial neural networks / Biology and Life Sciences / Cerebral Cortex - physiology / Circuits / Computational Biology / Computational mathematics / Computational neuroscience / Computer and Information Sciences / Computer architecture / Excitation / Machine learning / Mathematical models / Models, Neurological / Nerve Net - physiology / Neural circuitry / Neural networks / Neural Networks, Computer / Neurons / Neurons - physiology / Nonlinear Dynamics / Physical Sciences / Population / Prefrontal cortex / Representations / Simulation / Software / Stimuli / Synapses - physiology