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Inhibition stabilization is a widespread property of cortical networks.

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Date
2020-06-29
Authors
Sanzeni, Alessandro
Akitake, Bradley
Goldbach, Hannah C
Leedy, Caitlin E
Brunel, Nicolas
Histed, Mark H
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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 article
Subject
Motor Cortex
Visual Cortex
Somatosensory Cortex
Nerve Net
Interneurons
Animals
Animals, Genetically Modified
Mice
Parvalbumins
Neural Inhibition
Female
Male
Permalink
https://hdl.handle.net/10161/23344
Published Version (Please cite this version)
10.7554/elife.54875
Publication 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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Scholars@Duke

Brunel

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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