The Computational Structure of Spike Trains

Details The Computational Structure of Spike Trains The Computational Structure of Spike Trains

2009-12-30

arxiv.org/abs/1001.0036v1

Neural Computation, vol. 22 (2010), pp. 121--157

Biology

Quantitative Biology

Neurons and Cognition

Somewhat different format from journal version but same content

Scientific paper

10.1162/neco.2009.12-07-678

Neurons perform computations, and convey the results of those computations through the statistical structure of their output spike trains. Here we present a practical method, grounded in the information-theoretic analysis of prediction, for inferring a minimal representation of that structure and for characterizing its complexity. Starting from spike trains, our approach finds their causal state models (CSMs), the minimal hidden Markov models or stochastic automata capable of generating statistically identical time series. We then use these CSMs to objectively quantify both the generalizable structure and the idiosyncratic randomness of the spike train. Specifically, we show that the expected algorithmic information content (the information needed to describe the spike train exactly) can be split into three parts describing (1) the time-invariant structure (complexity) of the minimal spike-generating process, which describes the spike train statistically; (2) the randomness (internal entropy rate) of the minimal spike-generating process; and (3) a residual pure noise term not described by the minimal spike-generating process. We use CSMs to approximate each of these quantities. The CSMs are inferred nonparametrically from the data, making only mild regularity assumptions, via the causal state splitting reconstruction algorithm. The methods presented here complement more traditional spike train analyses by describing not only spiking probability and spike train entropy, but also the complexity of a spike train's structure. We demonstrate our approach using both simulated spike trains and experimental data recorded in rat barrel cortex during vibrissa stimulation.

Affiliated with

Also associated with

No associations

Rating

The Computational Structure of Spike Trains does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with The Computational Structure of Spike Trains, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and The Computational Structure of Spike Trains will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-103816