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Publication : Association cortical areas in the mouse contain a large population of fast-spiking GABAergic neurons that do not express parvalbumin.

First Author  Courcelles EJ Year  2024
Journal  Eur J Neurosci Volume  59
Issue  12 Pages  3236-3255
PubMed ID  38643976 Mgi Jnum  J:359484
Mgi Id  MGI:7788297 Doi  10.1111/ejn.16341
Citation  Courcelles EJ, et al. (2024) Association cortical areas in the mouse contain a large population of fast-spiking GABAergic neurons that do not express parvalbumin. Eur J Neurosci 59(12):3236-3255
abstractText  GABAergic neurons represent 10-15% of the neuronal population of the cortex but exert a powerful control over information flow in cortical circuits. The largest GABAergic class in the neocortex is represented by the parvalbumin-expressing fast-spiking neurons, which provide powerful somatic inhibition to their postsynaptic targets. Recently, the density of parvalbumin interneurons has been shown to be lower in associative areas of the mouse cortex as compared with sensory and motor areas. Modelling work based on these quantifications linked the low-density of parvalbumin interneurons with specific computations of associative cortices. However, it is still unknown whether the total GABAergic population of association cortices is smaller or whether another GABAergic type can compensate for the low density of parvalbumin interneurons. In the present study, we investigated these hypotheses using a combination of neuroanatomy, mouse genetics and neurophysiology. We found that the GABAergic population of association areas is comparable with that of primary sensory areas, and it is enriched of fast-spiking neurons that do not express parvalbumin and were not accounted for by previous quantifications. We developed an intersectional viral strategy to demonstrate that the population of fast-spiking neurons is comparable across cortical regions. Our results provide quantifications of the density of fast-spiking GABAergic neurons and offers new biological constrains to refine current models of cortical computations.
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