ArticleNeuron2018
The Dynamical Regime of Sensory Cortex: Stable Dynamics around a Single Stimulus-Tuned Attractor Account for Patterns of Noise Variability.
Article in Neuron, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 94 papers.
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Who cites it
94 citing papers in PubMed.
- Convergent multidimensional neural phenotypes in human patients and schizophrenia-relevant mouse models identify reduced recurrent excitation in cortical circuits.medRxiv : the preprint server for health sciences · 2026Article
- Embedding of low-dimensional sensory dynamics in recurrent networks: Implications for the geometry of neural representation.Journal of computational neuroscience · 2026Article
- Intrinsic timing, not temporal prediction, underlies ramping dynamics in visual and parietal cortex during passive behavior.Science advances · 2026Article
- Interactions across hemispheres in prefrontal cortex reflect global cognitive processing.Nature communications · 2026Article
- Laminar-specific control of response gain and orientation-tuning by parvalbumin-expressing inhibitory interneurons in primate visual cortex.Research square · 2026Article
- Size tuning of neural response variability in laminar circuits of macaque primary visual cortex.eLife · 2026Article
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- Voltage imaging as a window into neural computation.Neurophotonics · 2026Article
- Neuronal heterogeneity of normalization strength in a circuit model.Science advances · 2026Article
- Neural mechanisms of flexible perceptual inference.PLoS computational biology · 2025Article
- Noradrenergic Slow Vasomotion: The Hidden Fluid Pump Linking Sleep, Brain Clearance, and Dementia Pathogenesis.International journal of molecular sciences · 2025Review
- The fluctuation-based regime of thalamocortical circuitry.bioRxiv : the preprint server for biology · 2025Article
- Top-down perceptual inference shaping the activity of early visual cortex.Nature communications · 2025Article
- Subthreshold moment analysis of neuronal populations driven by synchronous synaptic inputs.PLoS computational biology · 2025Article
- Interleaving asynchronous and synchronous activity in balanced cortical networks with short term synaptic depression.Nature communications · 2025Article
- Global and local origins of trial-to-trial spike count variability in visual cortex.bioRxiv : the preprint server for biology · 2025Article
- Relating natural image statistics to patterns of response covariability in macaque primary visual cortex.Nature communications · 2025Article
- Brain-like variational inference.ArXiv · 2025Article
- A neural geometry approach comprehensively explains apparently conflicting models of visual perceptual learning.Nature human behaviour · 2025Article
- Untangling stability and gain modulation in cortical circuits with multiple interneuron classes.eLife · 2025Article
34 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
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.
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