title: Minimal size of cell assemblies coordinated by gamma oscillations.
abstract: In networks of excitatory and inhibitory neurons with mutual synaptic coupling, specific drive to sub-ensembles of cells often leads to gamma-frequency (25-100 Hz) oscillations. When the number of driven cells is too small, however, the synaptic interactions may not be strong or homogeneous enough to support the mechanism underlying the rhythm. Using a combination of computational simulation and mathematical analysis, we study the breakdown of gamma rhythms as the driven ensembles become too small, or the synaptic interactions become too weak and heterogeneous. Heterogeneities in drives or synaptic strengths play an important role in the breakdown of the rhythms; nonetheless, we find that the analysis of homogeneous networks yields insight into the breakdown of rhythms in heterogeneous networks. In particular, if parameter values are such that in a homogeneous network, it takes several gamma cycles to converge to synchrony, then in a similar, but realistically heterogeneous network, synchrony breaks down altogether. This leads to the surprising conclusion that in a network with realistic heterogeneity, gamma rhythms based on the interaction of excitatory and inhibitory cell populations must arise either rapidly, or not at all. For given synaptic strengths and heterogeneities, there is a (soft) lower bound on the possible number of cells in an ensemble oscillating at gamma frequency, based simply on the requirement that synaptic interactions between the two cell populations be strong enough. This observation suggests explanations for recent experimental results concerning the modulation of gamma oscillations in macaque primary visual cortex by varying spatial stimulus size or attention level, and for our own experimental results, reported here, concerning the optogenetic modulation of gamma oscillations in kainate-activated hippocampal slices. We make specific predictions about the behavior of pyramidal cells and fast-spiking interneurons in these experiments.
authors.affiliation: Department of Mathematics, Tufts University, Medford, Massachusetts, United States of America. cborgers@tufts.edu
authors.forename: Christoph
authors.initials: C
authors.lastname: Börgers
authors.forename: Giovanni
authors.initials: G
authors.lastname: Talei Franzesi
authors.forename: Fiona E N
authors.initials: FE
authors.lastname: Lebeau
authors.forename: Edward S
authors.initials: ES
authors.lastname: Boyden
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Kopell
mesh_terms: D000818:Animals; D058256:Brain Waves / Q000502:physiology*; D056654:CA3 Region, Hippocampal / Q000502:physiology*; D000075402:Channelrhodopsins; D019295:Computational Biology; D003198:Computer Simulation; D007608:Kainic Acid / Q000494:pharmacology; D008027:Light; D008251:Macaca; D051379:Mice; D008810:Mice, Inbred C57BL; D008959:Models, Neurological; D059010:Single-Cell Analysis; D013569:Synapses / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology
chemical_list: D000075402:Channelrhodopsins; D007608:Kainic Acid