The most likely voltage path and large deviations approximations for integrate-and-fire neurons

Liam Paninski

Journal of Computational Neuroscience 21: 71-87.

We develop theory and numerical methods for computing the most likely subthreshold voltage path of a noisy integrate-and-fire (IF) neuron, given observations of the neuron's superthreshold spiking activity. This optimal voltage path satisfies a second-order ordinary differential (Euler-Lagrange) equation which may be solved analytically in a number of special cases, and which may be solved numerically in general via a simple ``shooting'' algorithm. Our results are applicable for both linear and nonlinear subthreshold dynamics, and in certain cases may be extended to correlated subthreshold noise sources. We also show how this optimal voltage may be used to: 1) help sample efficiently from the conditional subthreshold noise distribution; 2) obtain approximations to the likelihood that an IF cell with a given set of parameters was responsible for the observed spike train; and 3) obtain approximations to the instantaneous firing rate and interspike interval distribution of a given noisy IF cell. The latter probability approximations are based on the classical Freidlin-Wentzell theory of large deviations principles for stochastic differential equations. We close by comparing this most likely voltage path to the true observed subthreshold voltage trace in a case when intracellular voltage recordings are available in vitro.
Reprint (580K, pdf)  |  Related work on integrate-and-fire models  |  Liam Paninski's home