A cortical circuit for gain control by behavioral state

By Yu Fu, J. Tucciarone, J. S. Espinosa, Nengyin Sheng, Daniel P. Darcy, R. Nicoll, Z. J. Huang, Michael P. Stryker. Cell, 2014

922 citations recorded by the source corpus.

The brain’s response to sensory input is strikingly modulated by behavioral state. Notably, the visual response of mouse primary visual cortex (V1) is enhanced by locomotion, a tractable and accessible example of a time-locked change in cortical state. The neural circuits that transmit behavioral state to sensory cortex to produce this modulation are unknown. In vivo calcium imaging of behaving animals revealed that locomotion activates vasoactive intestinal peptide (VIP)-positive neurons in mouse V1 independent of visual stimulation and largely through nicotinic inputs from basal forebrain. Optogenetic activation of VIP neurons increased V1 visual responses in stationary awake mice, artificially mimicking the effect of locomotion, and photolytic damage of VIP neurons abolished the enhancement of V1 responses by locomotion. These findings establish a cortical circuit for the enhancement of visual response by locomotion and provide a potential common circuit for the modulation of sensory processing by behavioral state.

Subfields: Cognitive Neuroscience, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/2b187f3b3326c4537740c5d8a08387cfc450e7df

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In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Pupil fluctuations track fast switching of cortical states during quiet wakefulness (2014, positive): “High-frequency oscillations in the gamma range were also enhanced during activity compared to quiet wakefulness (Figure S1B), consistent with previous reports (Niell and Stryker, 2010).”
A Sensorimotor Circuit in Mouse Cortex for Visual Flow Predictions. (2017, neutral): “Compatible with the idea of gain modulation, both cholinergic (Fu et al., 2014) and noradrenergic (Polack et al.”
The Effects of Acute Exercise on Mood, Cognition, Neurophysiology, and Neurochemical Pathways: A Review (2016, background): “This work corroborates studies showing that exercise shifts cortical circuits into excitatory states through GABAergic interneuron-mediated disinhibition of pyramidal neurons [234].”
Arousal and locomotion make distinct contributions to cortical activity patterns and visual encoding (2014, positive): “Recent work has highlighted distinct patterns of state-dependent recruitment of different interneuron classes (Fu et al., 2014; Gentet et al., 2012; Polack et al., 2013; Reimer, 2014; Zhou et al., 2014).”
Parallel processing by cortical inhibition enables context-dependent behavior (2016, neutral): “These three interneuron subtypes are thought to make up a canonical cortical circuit 7,31,36 .”
A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging (2016, methodology): “Calcium imaging samples activity of all labeled neurons in an imaging volume and can readily be combined with visualization of cell type markers to analyze activity in specific cell types, the nodes of neural circuits (Kerlin et al. 2010, Fu et al. 2014, Peron et al. 2015a).”
Cortical membrane potential signature of optimal states for sensory signal detection (2015, background): “One possible circuit involved in arousal-associated changes in cortical responsiveness is the cholinergic activation of inhibitory interneurons in layer 1, whose activity inhibits the activity of other classes of interneurons in layers 2/3 (Fu et al., 2014; Lee et al., 2013; Pi et al., 2013).”
Predictive Processing: A Canonical Cortical Computation (2018, positive): “, 2015) is thought to be the consequence of neuromodulatory inputs (Fu et al., 2014; Polack et al., 2013), which exert context dependent influence on responses in visual cortex (Pakan et al.”
Waking State: Rapid Variations Modulate Neural and Behavioral Responses (2015, positive): “VIP interneurons become highly active in relation to locomotion, whisking, or reinforcement signals (Fu et al., 2014; Lee et al., 2013; Pi et al., 2013).”