title: What determines the frequency of fast network oscillations with irregular neural discharges? I. Synaptic dynamics and excitation-inhibition balance.
abstract: When the local field potential of a cortical network displays coherent fast oscillations ( approximately 40-Hz gamma or approximately 200-Hz sharp-wave ripples), the spike trains of constituent neurons are typically irregular and sparse. The dichotomy between rhythmic local field and stochastic spike trains presents a challenge to the theory of brain rhythms in the framework of coupled oscillators. Previous studies have shown that when noise is large and recurrent inhibition is strong, a coherent network rhythm can be generated while single neurons fire intermittently at low rates compared to the frequency of the oscillation. However, these studies used too simplified synaptic kinetics to allow quantitative predictions of the population rhythmic frequency. Here we show how to derive quantitatively the coherent oscillation frequency for a randomly connected network of leaky integrate-and-fire neurons with realistic synaptic parameters. In a noise-dominated interneuronal network, the oscillation frequency depends much more on the shortest synaptic time constants (delay and rise time) than on the longer synaptic decay time, and approximately 200-Hz frequency can be realized with synaptic time constants taken from slice data. In a network composed of both interneurons and excitatory cells, the rhythmogenesis is a compromise between two scenarios: the fast purely interneuronal mechanism, and the slower feedback mechanism (relying on the excitatory-inhibitory loop). The properties of the rhythm are determined essentially by the ratio of time scales of excitatory and inhibitory currents and by the balance between the mean recurrent excitation and inhibition. Faster excitation than inhibition, or a higher excitation/inhibition ratio, favors the feedback loop and a much slower oscillation (typically in the gamma range).
authors.affiliation: Centre National de la Recherche Scientifique-Neurophysique et Physiologie du Système Moteur-Université Paris René Descartes, 75270 Paris Cedex 06, France. brunel@biomedicale.univ-paris5.fr
authors.forename: Nicolas
authors.initials: N
authors.lastname: Brunel
authors.forename: Xiao-Jing
authors.initials: XJ
authors.lastname: Wang
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D019706:Excitatory Postsynaptic Potentials; D006801:Humans; D007395:Interneurons / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D010507:Periodicity; D017966:Pyramidal Cells / Q000502:physiology*; D018091:Receptors, AMPA / Q000502:physiology; D016194:Receptors, N-Methyl-D-Aspartate / Q000502:physiology; D009435:Synaptic Transmission; D013997:Time Factors; D005680:gamma-Aminobutyric Acid / Q000502:physiology
chemical_list: D018091:Receptors, AMPA; D016194:Receptors, N-Methyl-D-Aspartate; D005680:gamma-Aminobutyric Acid