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A survey of neurophysiological differentiation across mouse visual brain areas and timescales

Authors :
Saurabh R. Gandhi
William G. P. Mayner
William Marshall
Yazan N. Billeh
Corbett Bennett
Samuel D. Gale
Chris Mochizuki
Joshua H. Siegle
Shawn Olsen
Giulio Tononi
Christof Koch
Anton Arkhipov
Source :
Frontiers in Computational Neuroscience, Vol 17 (2023)
Publication Year :
2023
Publisher :
Frontiers Media S.A., 2023.

Abstract

Neurophysiological differentiation (ND), a measure of the number of distinct activity states that a neural population visits over a time interval, has been used as a correlate of meaningfulness or subjective perception of visual stimuli. ND has largely been studied in non-invasive human whole-brain recordings where spatial resolution is limited. However, it is likely that perception is supported by discrete neuronal populations rather than the whole brain. Therefore, here we use Neuropixels recordings from the mouse brain to characterize the ND metric across a wide range of temporal scales, within neural populations recorded at single-cell resolution in localized regions. Using the spiking activity of thousands of simultaneously recorded neurons spanning 6 visual cortical areas and the visual thalamus, we show that the ND of stimulus-evoked activity of the entire visual cortex is higher for naturalistic stimuli relative to artificial ones. This finding holds in most individual areas throughout the visual hierarchy. Moreover, for animals performing an image change detection task, ND of the entire visual cortex (though not individual areas) is higher for successful detection compared to failed trials, consistent with the assumed perception of the stimulus. Together, these results suggest that ND computed on cellular-level neural recordings is a useful tool highlighting cell populations that may be involved in subjective perception.

Details

Language :
English
ISSN :
16625188
Volume :
17
Database :
Directory of Open Access Journals
Journal :
Frontiers in Computational Neuroscience
Publication Type :
Academic Journal
Accession number :
edsdoj.6ed87c60f8f9471ab3ea2cd7e8abb6bc
Document Type :
article
Full Text :
https://doi.org/10.3389/fncom.2023.1040629