title: Neural Sequence Generation Using Spatiotemporal Patterns of Inhibition.
abstract: Stereotyped sequences of neural activity are thought to underlie reproducible behaviors and cognitive processes ranging from memory recall to arm movement. One of the most prominent theoretical models of neural sequence generation is the synfire chain, in which pulses of synchronized spiking activity propagate robustly along a chain of cells connected by highly redundant feedforward excitation. But recent experimental observations in the avian song production pathway during song generation have shown excitatory activity interacting strongly with the firing patterns of inhibitory neurons, suggesting a process of sequence generation more complex than feedforward excitation. Here we propose a model of sequence generation inspired by these observations in which a pulse travels along a spatially recurrent excitatory chain, passing repeatedly through zones of local feedback inhibition. In this model, synchrony and robust timing are maintained not through redundant excitatory connections, but rather through the interaction between the pulse and the spatiotemporal pattern of inhibition that it creates as it circulates the network. These results suggest that spatially and temporally structured inhibition may play a key role in sequence generation.
authors.affiliation: Department of Mathematics, Boston University, Boston, Massachusetts, United States of America.|Department of Biology, Brandeis University, Waltham, Massachusetts, United States of America.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Cannon
authors.affiliation: Department of Mathematics, Boston University, Boston, Massachusetts, United States of America.
authors.forename: Nancy
authors.initials: N
authors.lastname: Kopell
authors.affiliation: Department of Biology, Boston University, Boston, Massachusetts, United States of America.
authors.forename: Timothy
authors.initials: T
authors.lastname: Gardner
authors.affiliation: Department of Biology, Boston University, Boston, Massachusetts, United States of America.
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Markowitz
mesh_terms: D000818:Animals; D001717:Birds; D019295:Computational Biology; D003198:Computer Simulation; D025461:Feedback, Physiological / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D009044:Motor Cortex / Q000502:physiology; D009433:Neural Inhibition / Q000502:physiology*