Integration of cortical population signals for visual perception

Visual stimuli evoke heterogeneous responses across nearby neural populations. These signals must be locally integrated to contribute to perception, but the principles underlying this process are unknown. Here, we exploit the systematic organization of orientation preference in macaque primary visua...

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Veröffentlicht in:Nature communications 2019-08, Vol.10 (1), p.3832-13, Article 3832
Hauptverfasser: Andrei, Ariana R., Pojoga, Sorin, Janz, Roger, Dragoi, Valentin
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Sprache:eng
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Zusammenfassung:Visual stimuli evoke heterogeneous responses across nearby neural populations. These signals must be locally integrated to contribute to perception, but the principles underlying this process are unknown. Here, we exploit the systematic organization of orientation preference in macaque primary visual cortex (V1) and perform causal manipulations to examine the limits of signal integration. Optogenetic stimulation and visual stimuli are used to simultaneously drive two neural populations with overlapping receptive fields. We report that optogenetic stimulation raises firing rates uniformly across conditions, but improves the detection of visual stimuli only when activating cells that are preferentially-tuned to the visual stimulus. Further, we show that changes in correlated variability are exclusively present when the optogenetically and visually-activated populations are functionally-proximal, suggesting that correlation changes represent a hallmark of signal integration. Our results demonstrate that information from functionally-proximal neurons is pooled for perception, but functionally-distal signals remain independent. Primary visual cortical neurons exhibit diverse responses to visual stimuli yet how these signals are integrated during visual perception is not well understood. Here, the authors show that optogenetic stimulation of neurons situated near the visually‐driven population leads to improved orientation detection in monkeys through changes in correlated variability.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-019-11736-2