Efficient estimation of hidden state dynamics from spike trains

Part of Advances in Neural Information Processing Systems 18 (NIPS 2005)

Bibtex Metadata Paper


Marton G. Danoczy, Richard Hahnloser


Neurons can have rapidly changing spike train statistics dictated by the underlying network excitability or behavioural state of an animal. To estimate the time course of such state dynamics from single- or multi- ple neuron recordings, we have developed an algorithm that maximizes the likelihood of observed spike trains by optimizing the state lifetimes and the state-conditional interspike-interval (ISI) distributions. Our non- parametric algorithm is free of time-binning and spike-counting prob- lems and has the computational complexity of a Mixed-state Markov Model operating on a state sequence of length equal to the total num- ber of recorded spikes. As an example, we fit a two-state model to paired recordings of premotor neurons in the sleeping songbird. We find that the two state-conditional ISI functions are highly similar to the ones mea- sured during waking and singing, respectively.