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Population-level interactions among PV, SOM, and Pyramidal neurons in cortex
The inhibitory neuron population of the cortex can be subdivided into multiple cell classes with highly specialized local circuitry, gene expression, and response properties. PV and SOM neurons are two nonoverlapping cell classes with distinct but interacting functional roles, that depend on brain s...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9882008/ https://www.ncbi.nlm.nih.gov/pubmed/36711788 http://dx.doi.org/10.1101/2023.01.09.523298 |
Sumario: | The inhibitory neuron population of the cortex can be subdivided into multiple cell classes with highly specialized local circuitry, gene expression, and response properties. PV and SOM neurons are two nonoverlapping cell classes with distinct but interacting functional roles, that depend on brain state. Here, we have applied a simple approach to identify PV, SOM, and putative pyramidal (Pyr) neurons within the same mice. We imaged their spike-related calcium activity in the posterior parietal cortex (PPC) while mice voluntarily ran on a spherical treadmill. We then related the activity of the simultaneously imaged neurons to each other, revealing that the activity of all inhibitory neurons was positively correlated compared to the activity within the Pyr population, and correlations were strongest among neurons of the same type. Furthermore, these activity relationships decayed with distance when comparing Pyr and inhibitory neurons, but not PV and SOM neurons. Finally, we identified coordinated activity events that were mostly restricted to either the PV or the SOM population, and used dimensionality reduction tools to reveal that these PV and SOM events were associated with different activity states in the Pyr population. This methodology will be useful to study population-level interactions across cell types in cortical circuits, and how they depend on behavioral state and task engagement. |
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