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Circuit models of low dimensional shared variability in cortical networks

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What this work says

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

inferredcites →https://doi.org/10.1007/s10827-009-0208-9openalex-snapshot

Local field potentials indicate network state and account for neuronal response variability

inferredcites →https://doi.org/10.1016/j.cell.2015.11.038openalex-snapshot

Prefrontal Parvalbumin Neurons in Control of Attention

inferredcites →https://doi.org/10.1016/j.neuron.2011.04.029openalex-snapshot

Using Neuronal Populations to Study the Mechanisms Underlying Spatial and Feature Attention

inferredcites →https://doi.org/10.1016/j.neuron.2012.06.029openalex-snapshot

Traveling Waves in Visual Cortex

inferredcites →https://doi.org/10.1016/j.neuron.2014.02.006openalex-snapshot

State Dependence of Noise Correlations in Macaque Primary Visual Cortex

inferredcites →https://doi.org/10.1016/j.neuron.2015.06.035openalex-snapshot

The Nature of Shared Cortical Variability

inferredcites →https://doi.org/10.1016/j.neuron.2016.06.028openalex-snapshot

Bottom-Up and Top-Down Input Augment the Variability of Cortical Neurons

inferredcites →https://doi.org/10.1016/j.tics.2018.02.011openalex-snapshot

Normalization and the Cholinergic Microcircuit: A Unified Basis for Attention

inferredcites →https://doi.org/10.1038/nature09086openalex-snapshot

Sensitivity to perturbations in vivo implies high noise and suggests rate coding in cortex

inferredcites →https://doi.org/10.1038/nature14273openalex-snapshot

Diverse coupling of neurons to populations in sensory cortex

inferredcites →https://doi.org/10.1038/ncomms7177openalex-snapshot

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

inferredcites →https://doi.org/10.1038/nn.2439openalex-snapshot

Attention improves performance primarily by reducing interneuronal correlations

inferredcites →https://doi.org/10.1038/nn.2842openalex-snapshot

Measuring and interpreting neuronal correlations

inferredcites →https://doi.org/10.1038/nn.3776openalex-snapshot

Dimensionality reduction for large-scale neural recordings

inferredcites →https://doi.org/10.1038/nn.4242openalex-snapshot

The mechanics of state-dependent neural correlations

inferredcites →https://doi.org/10.1038/nn.4433openalex-snapshot

The spatial structure of correlated neuronal variability

inferredcites →https://doi.org/10.1038/nn.4436openalex-snapshot

Parallel processing by cortical inhibition enables context-dependent behavior

inferredcites →https://doi.org/10.1038/nn.4455openalex-snapshot

Correlations demystified

inferredcites →https://doi.org/10.1038/nn1391openalex-snapshot

Invariant computations in local cortical networks with balanced excitation and inhibition

inferredcites →https://doi.org/10.1038/s41593-018-0089-1openalex-snapshot

Feedback determines the structure of correlated variability in primary visual cortex

inferredcites →https://doi.org/10.1073/pnas.1300098110openalex-snapshot

Stimulus-dependent variability and noise correlations in cortical MT neurons

inferredcites →https://doi.org/10.1088/0034-4885/61/4/002openalex-snapshot

Neural networks as spatio-temporal pattern-forming systems

inferredcites →https://doi.org/10.1093/cercor/7.3.237openalex-snapshot

Model of global spontaneous activity and local structured activity during delay periods in the cerebral cortex

inferredcites →https://doi.org/10.1101/086256openalex-snapshot

Feedback Determines the Structure of Correlated Variability in Primary Visual Cortex

inferredcites →https://doi.org/10.1101/094334openalex-snapshot

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

inferredcites →https://doi.org/10.1103/physrevlett.118.018103openalex-snapshot

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

inferredcites →https://doi.org/10.1103/physrevx.2.041007openalex-snapshot

Dynamic Flux Tubes Form Reservoirs of Stability in Neuronal Circuits

inferredcites →https://doi.org/10.1103/physrevx.4.021039openalex-snapshot

Balanced Networks of Spiking Neurons with Spatially Dependent Recurrent Connections

inferredcites →https://doi.org/10.1103/revmodphys.65.851openalex-snapshot

Pattern formation outside of equilibrium

inferredcites →https://doi.org/10.1126/science.1179850openalex-snapshot

The Asynchronous State in Cortical Circuits

inferredcites →https://doi.org/10.1126/science.274.5293.1724openalex-snapshot

Chaos in Neuronal Networks with Balanced Excitatory and Inhibitory Activity

inferredcites →https://doi.org/10.1126/science.aao0284openalex-snapshot

Learning and attention reveal a general relationship between population activity and behavior

inferredcites →https://doi.org/10.1146/annurev-statistics-041715-033733openalex-snapshot

Computational Neuroscience: Mathematical and Statistical Perspectives

inferredcites →https://doi.org/10.1152/jn.00845.2002openalex-snapshot

Cellular and Network Mechanisms of Slow Oscillatory Activity (<1 Hz) and Wave Propagations in a Cortical Network Model

inferredcites →https://doi.org/10.1371/journal.pbio.1002512openalex-snapshot

Spatial Embedding and Wiring Cost Constrain the Functional Layout of the Cortical Network of Rodents and Primates

inferredcites →https://doi.org/10.1371/journal.pcbi.1005141openalex-snapshot

Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models

inferredcites →https://doi.org/10.1523/jneurosci.0610-16.2016openalex-snapshot

Attention Increases Spike Count Correlations between Visual Cortical Areas

inferredcites →https://doi.org/10.1523/jneurosci.1669-14.2015openalex-snapshot

Propagating Waves Can Explain Irregular Neural Dynamics

inferredcites →https://doi.org/10.1523/jneurosci.17-06-02112.1997openalex-snapshot

Orientation Selectivity and the Arrangement of Horizontal Connections in Tree Shrew Striate Cortex

inferredcites →https://doi.org/10.1523/jneurosci.18-10-03870.1998openalex-snapshot

The Variable Discharge of Cortical Neurons: Implications for Connectivity, Computation, and Information Coding

inferredcites →https://doi.org/10.1523/jneurosci.2044-15.2016openalex-snapshot

On the Structure of Neuronal Population Activity under Fluctuations in Attentional State

inferredcites →https://doi.org/10.1523/jneurosci.3474-11.2012openalex-snapshot

Neural Correlation Is Stimulus Modulated by Feedforward Inhibitory Circuitry

inferredcites →https://doi.org/10.1523/jneurosci.4602-13.2014openalex-snapshot

Balanced Cortical Microcircuitry for Spatial Working Memory Based on Corrective Feedback Control

inferredcites →https://doi.org/10.1523/jneurosci.4994-13.2015openalex-snapshot

Cortical State Determines Global Variability and Correlations in Visual Cortex

inferredcites →https://doi.org/10.1523/jneurosci.5158-11.2012openalex-snapshot

Spatial Profile of Excitatory and Inhibitory Synaptic Connectivity in Mouse Primary Auditory Cortex

inferredcites →https://doi.org/10.7554/elife.08998openalex-snapshot

Attention stabilizes the shared gain of V4 populations

inferredcites →https://doi.org/10.7554/elife.19695openalex-snapshot

Inhibitory control of correlated intrinsic variability in cortical networks

inferredcites →https://doi.org/10.7554/elife.23978openalex-snapshot

Attentional modulation of neuronal variability in circuit models of cortex

What points at this work

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

inferred← citeshttps://doi.org/10.1016/j.conb.2018.12.009openalex-snapshot

Bridging large-scale neuronal recordings and large-scale network models using dimensionality reduction

inferred← citeshttps://doi.org/10.1016/j.isci.2024.110512openalex-snapshot

Circuit-motivated generalized affine models characterize stimulus-dependent visual cortical shared variability

inferred← citeshttps://doi.org/10.1016/j.tins.2018.07.015openalex-snapshot

Inhibitory Interneurons Regulate Temporal Precision and Correlations in Cortical Circuits

inferred← citeshttps://doi.org/10.1038/s41467-019-11633-8openalex-snapshot

Cortical reliability amid noise and chaos

inferred← citeshttps://doi.org/10.1038/s41467-021-26175-1openalex-snapshot

Spontaneous traveling waves naturally emerge from horizontal fiber time delays and travel through locally asynchronous-irregular states

inferred← citeshttps://doi.org/10.1038/s41467-023-43432-7openalex-snapshot

Targeted V1 comodulation supports task-adaptive sensory decisions

inferred← citeshttps://doi.org/10.1038/s41593-019-0477-1openalex-snapshot

Simultaneous multi-area recordings suggest that attention improves performance by reshaping stimulus representations

inferred← citeshttps://doi.org/10.1038/s41593-021-00822-8openalex-snapshot

Learning is shaped by abrupt changes in neural engagement

inferred← citeshttps://doi.org/10.1038/s41593-021-00980-9openalex-snapshot

Large-scale neural recordings call for new insights to link brain and behavior

inferred← citeshttps://doi.org/10.1038/s41593-023-01413-5openalex-snapshot

Propagation of activity through the cortical hierarchy and perception are determined by neural variability

inferred← citeshttps://doi.org/10.1101/2020.11.23.394916openalex-snapshot

Predicting perturbation effects from resting activity using functional causal flow

inferred← citeshttps://doi.org/10.1101/261016openalex-snapshot

Inferring and validating mechanistic models of neural microcircuits based on spike-train data

inferred← citeshttps://doi.org/10.1101/469114openalex-snapshot

Visual information is broadcast among cortical areas in discrete channels

inferred← citeshttps://doi.org/10.1103/physreve.99.052414openalex-snapshot

Correlated states in balanced neuronal networks

inferred← citeshttps://doi.org/10.1113/jp281510openalex-snapshot

Silences, spikes and bursts: Three‐part knot of the neural code

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