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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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A continuous entropy rate estimator for spike trains using a K-means-based context tree.

Tiger W Lin¹, George N Reeke

¹Revelle College, University of California, San Diego, La Jolla, CA 92092, USA. wulin@ucsd.edu

Neural Computation

|November 20, 2009

Summary

We developed a new method to estimate the entropy rate of neural spike trains using an inhomogeneous hidden Markov model. This approach accurately quantifies information changes in neuronal activity, outperforming existing methods.

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Area of Science:

Computational Neuroscience
Information Theory
Statistical Modeling

Background:

Neuronal spike trains are complex stochastic processes.
Understanding information processing in the brain requires quantifying the dynamics of these spike trains.
Existing methods for entropy rate estimation may not fully capture the intricacies of neuronal firing patterns.

Purpose of the Study:

To propose and validate a novel method for estimating the entropy rate of neuronal spike trains.
To leverage an inhomogeneous hidden Markov model (HMM) for improved accuracy in quantifying information changes.
To compare the performance of the proposed method against established entropy estimation techniques.

Main Methods:

Developed an inhomogeneous hidden Markov model (HMM) incorporating a context tree structure to model spike train subsequences.
Assumed gamma distributions for spike intervals within each Markov chain state, allowing for flexible distribution choices.
Employed bootstrapping on large raw data sequences to calculate entropy and confidence intervals.
Validated the estimator using synthetic data from multi-order Markov chains and experimental neuronal data.

Main Results:

The proposed entropy rate estimator converged to the theoretical Shannon entropy rate for synthetic data generated by multi-order Markov chains.
The method demonstrated robust performance on experimental neuronal spike train data.
The approach offers a potentially more accurate quantification of information dynamics in neural activity compared to other methods.

Conclusions:

The inhomogeneous hidden Markov model provides an effective framework for estimating the entropy rate of spike trains.
This method offers a valuable tool for analyzing information processing in neural systems.
Further research can explore alternative distributions and model complexities for enhanced analysis.