Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex
- University of Edinburgh, United Kingdom
Abstract
Cortical responses to sensory stimuli are modulated by behavioral state. In the primary visual cortex (V1), visual responses of pyramidal neurons increase during locomotion. This response gain was suggested to be mediated through inhibitory neurons, resulting in the disinhibition of pyramidal neurons. Using in vivo two-photon calcium imaging in layers 2/3 and 4 in mouse V1, we reveal that locomotion increases the activity of vasoactive intestinal peptide (VIP), somatostatin (SST) and parvalbumin (PV)-positive interneurons during visual stimulation, challenging the disinhibition model. In darkness, while most VIP and PV neurons remained locomotion responsive, SST and excitatory neurons were largely non-responsive. Context-dependent locomotion responses were found in each cell type, with the highest proportion among SST neurons. These findings establish that modulation of neuronal activity by locomotion is context-dependent and contest the generality of a disinhibitory circuit for gain control of sensory responses by behavioral state.
Article and author information
Author details
Nathalie L Rochefort
Funding
Wellcome (102857/Z/13/Z)
- Nathalie LI Rochefort
EuroSpin Erasmus Mundus Program
- Sander W Keemink
Royal Society (102857/Z/13/Z)
- Nathalie LI Rochefort
European Commission (Marie Curie Actions (FP7), MC-CIG 631770)
- Nathalie LI Rochefort
Patrick Wild Centre
- Nathalie LI Rochefort
The Shirley Foundation
- Nathalie LI Rochefort
RS MacDonald Charitable Trust (Seedcorn Grant 21)
- Nathalie LI Rochefort
University Of Edinburgh (Graduate School of Life Sciences)
- Evelyn Dylda
European Commission (Marie Curie Actions (FP7), IEF 624461)
- Janelle MP Pakan
EPSRC Doctoral Training Centre in Neuroinformatics (EP/F500385/1 and BB/F529254/1)
- Sander W Keemink
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Animal experimentation: All procedures were approved by the University of Edinburgh animal welfare committee, and were performed under a UK Home Office Project License (PPL No. 60/4570).
Copyright
© 2016, Pakan et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Behavioral-state modulation of inhibition is context-dependent and cell type specific in mouse visual cortex
eLife 5:e14985.
https://doi.org/10.7554/eLife.14985
Further reading
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- Neuroscience
Biological memory networks are thought to store information by experience-dependent changes in the synaptic connectivity between assemblies of neurons. Recent models suggest that these assemblies contain both excitatory and inhibitory neurons (E/I assemblies), resulting in co-tuning and precise balance of excitation and inhibition. To understand computational consequences of E/I assemblies under biologically realistic constraints we built a spiking network model based on experimental data from telencephalic area Dp of adult zebrafish, a precisely balanced recurrent network homologous to piriform cortex. We found that E/I assemblies stabilized firing rate distributions compared to networks with excitatory assemblies and global inhibition. Unlike classical memory models, networks with E/I assemblies did not show discrete attractor dynamics. Rather, responses to learned inputs were locally constrained onto manifolds that ‘focused’ activity into neuronal subspaces. The covariance structure of these manifolds supported pattern classification when information was retrieved from selected neuronal subsets. Networks with E/I assemblies therefore transformed the geometry of neuronal coding space, resulting in continuous representations that reflected both relatedness of inputs and an individual’s experience. Such continuous representations enable fast pattern classification, can support continual learning, and may provide a basis for higher-order learning and cognitive computations.
