title: Inhibitory stabilization and visual coding in cortical circuits with multiple interneuron subtypes.
abstract: Recent anatomical and functional characterization of cortical inhibitory interneurons has highlighted the diverse computations supported by different subtypes of interneurons. However, most theoretical models of cortex do not feature multiple classes of interneurons and rather assume a single homogeneous population. We study the dynamics of recurrent excitatory-inhibitory model cortical networks with parvalbumin (PV)-, somatostatin (SOM)-, and vasointestinal peptide-expressing (VIP) interneurons, with connectivity properties motivated by experimental recordings from mouse primary visual cortex. Our theory describes conditions under which the activity of such networks is stable and how perturbations of distinct neuronal subtypes recruit changes in activity through recurrent synaptic projections. We apply these conclusions to study the roles of each interneuron subtype in disinhibition, surround suppression, and subtractive or divisive modulation of orientation tuning curves. Our calculations and simulations determine the architectural and stimulus tuning conditions under which cortical activity consistent with experiment is possible. They also lead to novel predictions concerning connectivity and network dynamics that can be tested via optogenetic manipulations. Our work demonstrates that recurrent inhibitory dynamics must be taken into account to fully understand many properties of cortical dynamics observed in experiments.
authors.affiliation: Center for Theoretical Neuroscience, Columbia University, New York, New York; Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania; and ak3625@columbia.edu.
authors.forename: Ashok
authors.identifier: http://orcid.org/0000-0003-2422-6576
authors.initials: A
authors.lastname: Litwin-Kumar
authors.affiliation: Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, Indiana; Interdisciplinary Center for Network Science and Applications, University of Notre Dame, Notre Dame, Indiana; Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania; and.
authors.forename: Robert
authors.initials: R
authors.lastname: Rosenbaum
authors.affiliation: Department of Mathematics, University of Pittsburgh, Pittsburgh, Pennsylvania; and Center for the Neural Basis of Cognition, Pittsburgh, Pennsylvania.
authors.forename: Brent
authors.initials: B
authors.lastname: Doiron
mesh_terms: D000200:Action Potentials; D000818:Animals; D007395:Interneurons / Q000145:classification / Q000378:metabolism / Q000502:physiology*; D051379:Mice; D008959:Models, Neurological; D009415:Nerve Net / Q000166:cytology / Q000502:physiology; D009433:Neural Inhibition; D010320:Parvalbumins / Q000235:genetics / Q000378:metabolism; D013004:Somatostatin / Q000235:genetics / Q000378:metabolism; D055366:Synaptic Potentials; D014660:Vasoactive Intestinal Peptide / Q000235:genetics / Q000378:metabolism; D014793:Visual Cortex / Q000166:cytology / Q000502:physiology*; D014796:Visual Perception
chemical_list: D010320:Parvalbumins; D014660:Vasoactive Intestinal Peptide; D013004:Somatostatin
keywords: V1; inhibition; modeling