Yuriy Mishchenko Papers:
Monte Carlo approaches have recently been proposed to quantify and estimate connectivity in neuronal networks. The key problem is to sample from the conditional distribution of a single neuronal spike train, given the activity of the other neurons in the network. Dependencies between neurons are usually relatively weak; however, temporal dependencies within the spike train of a single neuron are typically strong. In this paper we develop several Metropolis-Hastings samplers which are specialized to take advantage of this dependency structure. These samplers are based on two ideas: 1) an adaptation of fast forward-backward algorithms from the theory of hidden Markov models to take advantage of the local dependencies inherent in spike trains, and 2) a first-order expansion of the conditional likelihood which allows for efficient exact sampling in the limit of weak coupling between neurons. We also demonstrate that these samplers can effectively incorporate side information, in particular noisy fluorescence observations in the context of calcium-sensitive imaging experiments. We quantify the efficiency of these samplers in a variety of simulated experiments in which the network parameters are closely matched to data measured in real cortical networks.
The implementations in Matlab of the methods discussed in the paper can be downloaded from here efficient_samplers_hidden_neuron_state.zip. Full text
Mishchenko Y., Paninski L. (2011) "Efficient methods for sampling spike trains in networks of coupled neurons.", Annals of Applied Statistics, 5, 1893