title: Intrinsic dynamics in neuronal networks. I. Theory.
abstract: Many networks in the mammalian nervous system remain active in the absence of stimuli. This activity falls into two main patterns: steady firing at low rates and rhythmic bursting. How are these firing patterns generated? Specifically, how do dynamic interactions between excitatory and inhibitory neurons produce these firing patterns, and how do networks switch from one firing pattern to the other? We investigated these questions theoretically by examining the intrinsic dynamics of large networks of neurons. Using both a semianalytic model based on mean firing rate dynamics and simulations with large neuronal networks, we found that the dynamics, and thus the firing patterns, are controlled largely by one parameter, the fraction of endogenously active cells. When no endogenously active cells are present, networks are either silent or fire at a high rate; as the number of endogenously active cells increases, there is a transition to bursting; and, with a further increase, there is a second transition to steady firing at a low rate. A secondary role is played by network connectivity, which determines whether activity occurs at a constant mean firing rate or oscillates around that mean. These conclusions require only conventional assumptions: excitatory input to a neuron increases its firing rate, inhibitory input decreases it, and neurons exhibit spike-frequency adaptation. These conclusions also lead to two experimentally testable predictions: 1) isolated networks that fire at low rates must contain endogenously active cells and 2) a reduction in the fraction of endogenously active cells in such networks must lead to bursting.
authors.affiliation: Department of Neurobiology, University of California at Los Angeles, Los Angeles, California 90095, USA.
authors.forename: P E
authors.initials: PE
authors.lastname: Latham
authors.forename: B J
authors.initials: BJ
authors.lastname: Richmond
authors.forename: P G
authors.initials: PG
authors.lastname: Nelson
authors.forename: S
authors.initials: S
authors.lastname: Nirenberg
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D003198:Computer Simulation; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D008322:Mammals; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010507:Periodicity