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Stabilized supralinear network dynamics account for stimulus-induced changes of noise variability in the cortex

https://doi.org/10.1101/094334
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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State Dependence of Noise Correlations in Macaque Primary Visual Cortex
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Sensory stimulation shifts visual cortex from synchronous to asynchronous states
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Attention improves performance primarily by reducing interneuronal correlations
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Stimulus onset quenches neural variability: a widespread cortical phenomenon
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Linear transformation of thalamocortical input by intracortical excitation
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Bump attractor dynamics in prefrontal cortex explains behavioral precision in spatial working memory
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Two types of asynchronous activity in networks of excitatory and inhibitory spiking neurons
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A hierarchy of intrinsic timescales across primate cortex
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Ceramide-orchestrated signalling in cancer cells
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Bulk antimony sulfide with excellent cycle stability as next-generation anode for lithium-ion batteries
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Motor preparation attenuates neural variability and beta-band LFP in parietal cortex
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Stimulus-dependent variability and noise correlations in cortical MT neurons
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Theory of orientation tuning in visual cortex.
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inferredcites →https://doi.org/10.1113/jphysiol.2001.012776openalex-snapshot
Laminar processing of stimulus orientation in cat visual cortex
inferredcites →https://doi.org/10.1126/science.1179850openalex-snapshot
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Chaotic Balanced State in a Model of Cortical Circuits
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Correlations and Population Dynamics in Cortical Networks
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Mathematical Equivalence of Two Common Forms of Firing Rate Models of Neural Networks
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Analysis of the Stabilized Supralinear Network
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Neural Network Mechanisms Underlying Stimulus Driven Variability Reduction
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What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1007/s10827-017-0667-3openalex-snapshot
Transitions between asynchronous and synchronous states: a theory of correlations in small neural circuits
inferred← citeshttps://doi.org/10.1038/s41598-018-27099-5openalex-snapshot
E-I balance emerges naturally from continuous Hebbian learning in autonomous neural networks
inferred← citeshttps://doi.org/10.1101/167916openalex-snapshot
Capturing the diversity of biological tuning curves using generative adversarial networks
inferred← citeshttps://doi.org/10.1101/217976openalex-snapshot
Circuit models of low dimensional shared variability in cortical networks
inferred← citeshttps://doi.org/10.1146/annurev-neuro-072116-031005openalex-snapshot
Inhibitory Plasticity: Balance, Control, and Codependence
inferred← citeshttps://doi.org/10.48550/arxiv.1610.03110openalex-snapshot
Characterizing variability in nonlinear recurrent neuronal networks
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