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The Dynamical Regime of Sensory Cortex:Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability
Title / Series / Name
Neuron
Publication Volume
98
Publication Issue
4
Pages
Editors
Keywords
MT
V1
circuit dynamics
cortical variability
noise correlations
theoretical neuroscience
variability quenching
General Neuroscience
V1
circuit dynamics
cortical variability
noise correlations
theoretical neuroscience
variability quenching
General Neuroscience
URI
https://hdl.handle.net/20.500.14018/28519
Abstract
Correlated variability in cortical activity is ubiquitously quenched following stimulus onset, in a stimulus-dependent manner. These modulations have been attributed to circuit dynamics involving either multiple stable states (“attractors”) or chaotic activity. Here we show that a qualitatively different dynamical regime, involving fluctuations about a single, stimulus-driven attractor in a loosely balanced excitatory-inhibitory network (the stochastic “stabilized supralinear network”), best explains these modulations. Given the supralinear input/output functions of cortical neurons, increased stimulus drive strengthens effective network connectivity. This shifts the balance from interactions that amplify variability to suppressive inhibitory feedback, quenching correlated variability around more strongly driven steady states. Comparing to previously published and original data analyses, we show that this mechanism, unlike previous proposals, uniquely accounts for the spatial patterns and fast temporal dynamics of variability suppression. Specifying the cortical operating regime is key to understanding the computations underlying perception. Stimuli suppress cortical correlated variability. Hennequin et al. show that a cortical operating regime of inhibitory stabilization around a single stable state—the “stabilized supralinear network”—explains this suppression's tuning and timing, while alternative proposed regimes do not.
Topic
Publisher
Place of Publication
Type
Journal article
Date
2018-05-16
Language
ISBN
Identifiers
10.1016/j.neuron.2018.04.017