title: Reproducible sequence generation in random neural ensembles.
abstract: Little is known about the conditions that neural circuits have to satisfy to generate reproducible sequences. Evidently, the genetic code cannot control all the details of the complex circuits in the brain. In this Letter, we give the conditions on the connectivity degree that lead to reproducible and robust sequences in a neural population of randomly coupled excitatory and inhibitory neurons. In contrast to the traditional theoretical view we show that the sequences do not need to be learned. In the framework proposed here just the averaged characteristics of the random circuits have to be under genetic control. We found that rhythmic sequences can be generated if random networks are in the vicinity of an excitatory-inhibitory synaptic balance. Reproducible transient sequences, on the other hand, are found far from a synaptic balance.
authors.affiliation: Institute for Nonlinear Science, University of California, San Diego, La Jolla, CA 92093-0402, USA.
authors.forename: Ramón
authors.initials: R
authors.lastname: Huerta
authors.forename: Mikhail
authors.initials: M
authors.lastname: Rabinovich
mesh_terms: D000200:Action Potentials; D000818:Animals; D001703:Biophysics / Q000379:methods*; D001921:Brain / Q000502:physiology; D006801:Humans; D008959:Models, Neurological; D015233:Models, Statistical; D008962:Models, Theoretical; D009415:Nerve Net; D009473:Neuronal Plasticity; D009474:Neurons / Q000502:physiology*; D009435:Synaptic Transmission