How Inhibition Shapes Cortical Activity

Isaacson, Jeffry S.; Scanziani, Massimo

doi:10.1016/j.neuron.2011.09.027

Cited by 1,678Neural dynamics and brain functionNeuroscience and Neuropharmacology ResearchVisual perception and processing mechanisms

How Inhibition Shapes Cortical Activity

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Isaacson, Jeffry S., Scanziani, Massimo·2011·Neuron

DOI 10.1016/j.neuron.2011.09.027PMC PMC3236361

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Abstract

Cortical processing reflects the interplay of synaptic excitation and synaptic inhibition. Rapidly accumulating evidence is highlighting the crucial role of inhibition in shaping spontaneous and sensory-evoked cortical activity and thus underscores how a better knowledge of inhibitory circuits is necessary for our understanding of cortical function. We discuss current views of how inhibition regulates the function of cortical neurons and point to a number of important open questions.

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The prevalence of inhibitory interneurons creates winner-take-all dynamics and competitive inhibition that force categorical rather than probabilistic representations.
“Furthermore, the prevalence of inhibitory interneurons creates winner-take-all dynamics and competitive inhibition that force categorical rather than probabilistic representations—a pattern seen in the sharp tuning curves and bistable activity patterns observed across cortical regions (Buzsáki 2006; Isaacson and Scanziani 2011).”
From The myth of the Bayesian brain (2025) · cites this paper as Isaacson and Scanziani 2011
The “Building blocks” section (page unavailable) describes cortical inhibitory interneurons and their widespread connections, and “Inhibition sharpens tuning” (page unavailable) explains how inhibition can sharpen stimulus tuning. The paper does not say these circuits create winner-take-all dynamics or force categorical rather than probabilistic representations. Its “Inhibition, Gain Control and Dynamic Range” section instead says inhibition can make population recruitment more progressive across a wider range of inputs. (LLM, conf=0.88)
Experimental — accuracy not yet measured
Sharp tuning curves and bistable activity patterns are observed across cortical regions.
“a pattern seen in the sharp tuning curves and bistable activity patterns observed across cortical regions (Buzsáki 2006; Isaacson and Scanziani 2011).”
From The myth of the Bayesian brain (2025) · cites this paper as Isaacson and Scanziani 2011
Glutamatergic and GABAergic dysfunction in the PFC and ACC has been extensively implicated in both MDD and TRD.
“Glutamatergic and GABAergic dysfunction in the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) has been extensively implicated in both MDD and TRD [16,17,18,19,20,21,22,23,24,25].”
From Pharmacotherapies Targeting GABA-Glutamate Neurotransmission for Treatment-Resistant Depression (2023) · cites this paper as [21]
An imbalance between L-glutamate and γ-aminobutyric acid (GABA) has a key role in the pathophysiology of TRD.
“An imbalance between two primary central nervous system (CNS) neurotransmitters, L-glutamate and γ-aminobutyric acid (GABA), has emerged as having a key role in the pathophysiology of TRD.”
From Pharmacotherapies Targeting GABA-Glutamate Neurotransmission for Treatment-Resistant Depression (2023) · cites this paper as [21]
Perisomatic inhibition efficiently controls Na+-dependent action potential generation, whereas dendritic inhibition influences local voltage-gated conductances, shunts excitatory inputs lying distal to their location, and regulates the generation of Ca2+-dependent action potentials and synaptic plasticity.
“Perisomatic inhibition efficiently controls Na+-dependent action potential generation, whereas dendritic inhibition influences local voltage-gated conductances, shunts excitatory inputs lying distal to their location, and regulates the generation of Ca2+-dependent action potentials and synaptic plasticity (522, 790, 721).”
From Hippocampal GABAergic Inhibitory Interneurons (2017) · cites this paper as [522]

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“…interneurons creates winner-take-all dynamics and competitive inhibition that force categorical rather than probabilistic represen-tations—a pattern seen in the sharp tuning curves and bistable activity patterns observed across cortical regions (Buzsáki 2006; Isaacson and Scanziani 2011).”

Authors: Isaacson, J. S., & Scanziani, M.
Affiliations: University of California San Diego (US)
Howard Hughes Medical Institute (US)
Corresponding author: Isaacson, Jeffry S.
Scanziani, Massimo
ORCID: Isaacson, Jeffry S.
Scanziani, Massimo
Venue: Neuron
Publisher: Elsevier BV
Published: 2011-10
Volume: 72
Issue: 2
Pages: 231-243
ISSN: 0896-6273 (ISSN-L)
DOI: 10.1016/j.neuron.2011.09.027
PMC: PMC3236361
OpenAlex: W1997953085
Cited by: 1,678
References: 126
Citations by year: 2026: 114 · 2025: 143 · 2024: 131 · 2023: 110 · 2022: 117 · 2021: 142 · 2020: 126 · 2019: 128 · 2018: 132 · 2017: 112 · 2016: 119 · 2015: 89 · 2014: 93 · 2013: 73 · 2012: 46
FWCI: 5.83
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (vor, 655 d)
Locations: Neuron (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Howard Hughes Medical Institute
National Institute on Deafness and Other Communication Disorders (R01DC004682, R01 DC04682)
National Institute of Neurological Disorders and Stroke (NS069010, RC1NS069010)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Neuroscience and Neuropharmacology Research
Life Sciences › Neuroscience › Cognitive Neuroscience › Visual perception and processing mechanisms
Keywords: cortical inhibition, synaptic inhibition, excitation-inhibition balance, inhibitory circuits, spontaneous cortical activity, sensory-evoked activity, cortical neurons, cortical function
MeSH terms: Animals, Cerebral Cortex, Membrane Potentials, Nerve Net, Neural Inhibition, Synaptic Transmission, Neurons

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How Inhibition Shapes Cortical Activity

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