Inhibition stabilization is a widespread property of cortical networks.
Abstract
Many cortical network models use recurrent coupling strong enough to require inhibition
for stabilization. Yet it has been experimentally unclear whether inhibition-stabilized
network (ISN) models describe cortical function well across areas and states. Here,
we test several ISN predictions, including the counterintuitive (paradoxical) suppression
of inhibitory firing in response to optogenetic inhibitory stimulation. We find clear
evidence for ISN operation in mouse visual, somatosensory, and motor cortex. Simple
two-population ISN models describe the data well and let us quantify coupling strength.
Although some models predict a non-ISN to ISN transition with increasingly strong
sensory stimuli, we find ISN effects without sensory stimulation and even during light
anesthesia. Additionally, average paradoxical effects result only with transgenic,
not viral, opsin expression in parvalbumin (PV)-positive neurons; theory and expression
data show this is consistent with ISN operation. Taken together, these results show
strong coupling and inhibition stabilization are common features of the cortex.
Type
Journal articleSubject
Motor CortexVisual Cortex
Somatosensory Cortex
Nerve Net
Interneurons
Animals
Animals, Genetically Modified
Mice
Parvalbumins
Neural Inhibition
Female
Male
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https://hdl.handle.net/10161/23344Published Version (Please cite this version)
10.7554/elife.54875Publication Info
Sanzeni, Alessandro; Akitake, Bradley; Goldbach, Hannah C; Leedy, Caitlin E; Brunel,
Nicolas; & Histed, Mark H (2020). Inhibition stabilization is a widespread property of cortical networks. eLife, 9. pp. 1-39. 10.7554/elife.54875. Retrieved from https://hdl.handle.net/10161/23344.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Nicolas Brunel
Professor of Neurobiology
We use theoretical models of brain systems to investigate how they process and learn
information from their inputs. Our current work focuses on the mechanisms of learning
and memory, from the synapse to the network level, in collaboration with various experimental
groups. Using methods fromstatistical physics, we have shown recently that the synapticconnectivity
of a network that maximizes storage capacity reproducestwo key experimentally observed
features: low connection proba

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