The Asynchronous State in Cortical Circuits

Renart, Alfonso; de la Rocha, Jaime; Bartho, Peter; Hollender, Liad; Parga, Néstor; Reyes, Alex; Harris, Kenneth D.

doi:10.1126/science.1179850

Cited by 1,106Neural dynamics and brain functionNeuroscience and Neuropharmacology Researchstochastic dynamics and bifurcation

The Asynchronous State in Cortical Circuits

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Renart, Alfonso, de la Rocha, Jaime, Bartho, Peter, Hollender, Liad, Parga, Néstor, Reyes, Alex, Harris, Kenneth D.·2010·Science

DOI 10.1126/science.1179850PMC PMC2861483

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Abstract

Columns, Connections, and Correlations: What is the nature of interactions between neurons in neural circuits? The prevalent hypothesis suggests that dense local connectivity causes nearby cortical neurons to receive substantial amounts of common input, which in turn leads to strong correlations between them. Now two studies challenge this view, which impacts our fundamental understanding of coding in the cortex. Ecker et al. (p. 584 ) investigated the statistics of correlated firing in pairs of neurons from area V1 of awake macaque monkeys. In contrast to previous studies, correlations turned out to be very low, irrespective of the stimulus being shown to the animals, the distances of the recording sites, and the similarity of the neuron's receptive fields or response properties. In an accompanying modeling and recording paper, Renart et al. (p. 587 ) demonstrate how it is possible to have zero noise correlation, even among cells with common input.

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Neural systems exhibit substantial intrinsic noise and variability at all levels, from ion channel fluctuations to large-scale network dynamics.
“Neural systems exhibit substantial intrinsic noise and variability at all levels, from ion channel fluctuations to large-scale network dynamics (Renart et al. 2010).”
From The myth of the Bayesian brain (2025) · cites this paper as Renart et al. 2010
The indexed passage labeled Abstract (page unavailable) describes spontaneous fluctuations in cortical network activity and irregular firing in a network model. It does not address ion-channel fluctuations or establish that intrinsic noise and variability occur at all levels of neural systems. (LLM, conf=0.84)
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Authors: Renart, A., de la Rocha, J., Bartho, P., Hollender, L., Parga, N., Reyes, A., & Harris, K. D.
Affiliations: Rutgers, The State University of New Jersey (US)
Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, NJ 07102, USA.
Universidad Autónoma de Madrid (ES)
New York University (US)
Corresponding author: Renart, Alfonso
Harris, Kenneth D.
ORCID: Renart, Alfonso
Parga, Néstor
Reyes, Alex
Venue: Science
Publisher: American Association for the Advancement of Science (AAAS)
Published: 2010-01-29
Volume: 327
Issue: 5965
Pages: 587-590
ISSN: 0036-8075 (ISSN-L)
1095-9203
DOI: 10.1126/science.1179850
PMC: PMC2861483
OpenAlex: W2106566258
Cited by: 1,106
References: 25
Citations by year: 2026: 48 · 2025: 68 · 2024: 57 · 2023: 50 · 2022: 49 · 2021: 70 · 2020: 74 · 2019: 93 · 2018: 74 · 2017: 84 · 2016: 78 · 2015: 83 · 2014: 69 · 2013: 75 · 2012: 53
FWCI: 39.96
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Locations: Science (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
UCL Discovery (University College London) (submittedVersion)
Funders: Engineering and Physical Sciences Research Council (EP/I005102/1)
National Institute of Mental Health (MH073245, R01MH073245)
National Institute on Deafness and Other Communication Disorders (DC005787-01A1, DC009947, R01DC005787, R01DC009947)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › stochastic dynamics and bifurcation
Keywords: asynchronous state, neural correlations, cortical circuits, recurrent neural networks, shared inputs, spontaneous fluctuations, excitation-inhibition balance, synaptic currents, information decoding, information processing
MeSH terms: Action Potentials, Algorithms, Animals, Cerebral Cortex, Computer Simulation, Models, Neurological, Nerve Net, Neural Inhibition, Neural Pathways, Synaptic Transmission, Neurons, Synapses, Rats, Sprague-Dawley, Excitatory Postsynaptic Potentials, Rats, Inhibitory Postsynaptic Potentials, Synaptic Potentials

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The Asynchronous State in Cortical Circuits

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