title: A first-passage-time analysis of the periodically forced noisy leaky integrate-and-fire model.
abstract: We present a general method for the analysis of the discharge trains of periodically forced noisy leaky integrate-and-fire neuron models. This approach relies on the iterations of a stochastic phase transition operator that generalizes the phase transition function used for the study of periodically forced deterministic oscillators to noisy systems. The kernel of this operator is defined in terms of the the first passage time probability density function of the Ornstein Uhlenbeck process through a suitable threshold. Numerically, it is computed as the solution of a singular integral equation. It is shown that, for the noisy system, quantities such as the phase distribution (cycle histogram), the interspike interval distribution, the autocorrelation function of the intervals, the autocorrelogram and the power spectrum density of the spike train, as well as the input-output cross-correlation and cross-spectral density can all be computed using the stochastic phase transition operator. A detailed description of the numerical implementation of the method, together with examples, is provided.
authors.affiliation: Graduate School of Engineering Science, Osaka University, Toyonaka, Japan. simokawa@bpe.es.osaka-u.ac.jp
authors.forename: T
authors.initials: T
authors.lastname: Shimokawa
authors.forename: K
authors.initials: K
authors.lastname: Pakdaman
authors.forename: T
authors.initials: T
authors.lastname: Takahata
authors.forename: S
authors.initials: S
authors.lastname: Tanabe
authors.forename: S
authors.initials: S
authors.lastname: Sato
mesh_terms: D000200:Action Potentials / Q000502:physiology*; D003491:Cybernetics; D008959:Models, Neurological; D009475:Neurons, Afferent / Q000502:physiology*; D010507:Periodicity; D011336:Probability; D013269:Stochastic Processes