title: Synchrony in excitatory neural networks.
abstract: Synchronization properties of fully connected networks of identical oscillatory neurons are studied, assuming purely excitatory interactions. We analyze their dependence on the time course of the synaptic interaction and on the response of the neurons to small depolarizations. Two types of responses are distinguished. In the first type, neurons always respond to small depolarization by advancing the next spike. In the second type, an excitatory postsynaptic potential (EPSP) received after the refractory period delays the firing of the next spike, while an EPSP received at a later time advances the firing. For these two types of responses we derive general conditions under which excitation destabilizes in-phase synchrony. We show that excitation is generally desynchronizing for neurons with a response of type I but can be synchronizing for responses of type II when the synaptic interactions are fast. These results are illustrated on three models of neurons: the Lapicque integrate-and-fire model, the model of Connor et al., and the Hodgkin-Huxley model. The latter exhibits a type II response, at variance with the first two models, that have type I responses. We then examine the consequences of these results for large networks, focusing on the states of partial coherence that emerge. Finally, we study the Lapicque model and the model of Connor et al. at large coupling and show that excitation can be desynchronizing even beyond the weak coupling regime.
authors.affiliation: Centre de Physique Théorique, UPR014 CNRS, Ecole Polytechnique, Palaiseau, France.
authors.forename: D
authors.initials: D
authors.lastname: Hansel
authors.forename: G
authors.initials: G
authors.lastname: Mato
authors.forename: C
authors.initials: C
authors.lastname: Meunier
mesh_terms: D003198:Computer Simulation; D004594:Electrophysiology; D005073:Evoked Potentials, Somatosensory / Q000502:physiology; D008959:Models, Neurological; D009431:Neural Conduction / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology