title: The ISI distribution of the stochastic Hodgkin-Huxley neuron.
abstract: The simulation of ion-channel noise has an important role in computational neuroscience. In recent years several approximate methods of carrying out this simulation have been published, based on stochastic differential equations, and all giving slightly different results. The obvious, and essential, question is: which method is the most accurate and which is most computationally efficient? Here we make a contribution to the answer. We compare interspike interval histograms from simulated data using four different approximate stochastic differential equation (SDE) models of the stochastic Hodgkin-Huxley neuron, as well as the exact Markov chain model simulated by the Gillespie algorithm. One of the recent SDE models is the same as the Kurtz approximation first published in 1978. All the models considered give similar ISI histograms over a wide range of deterministic and stochastic input. Three features of these histograms are an initial peak, followed by one or more bumps, and then an exponential tail. We explore how these features depend on deterministic input and on level of channel noise, and explain the results using the stochastic dynamics of the model. We conclude with a rough ranking of the four SDE models with respect to the similarity of their ISI histograms to the histogram of the exact Markov chain model.
authors.affiliation: Institute for Neural Computation, University of California San Diego, La Jolla, CA, USA.
authors.forename: Peter F
authors.initials: PF
authors.lastname: Rowat
authors.affiliation: Mathematics Department, University of British Columbia Vancouver, BC, Canada.
authors.forename: Priscilla E
authors.initials: PE
authors.lastname: Greenwood
keywords: Gillespie algorithm; Hodgkin-Huxley; ISI distribution; ISI histogram; Kurtz approximation; stochastic differential equation; stochastic dynamics