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The computational structure of spike trains.

Robert Haslinger¹, Kristina Lisa Klinkner, Cosma Rohilla Shalizi

¹Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA. robhh@nmr.mgh.harvard.edu

Neural Computation

|September 22, 2009

Summary

We developed a method to analyze neural spike trains using causal state models (CSMs). This approach quantifies the complexity and randomness within neural activity, offering new insights into neuronal computation.

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Area of Science:

Computational Neuroscience
Information Theory
Systems Neuroscience

Background:

Neurons communicate via spike trains, whose statistical structure encodes computational results.
Understanding this structure is key to deciphering neural computation.

Purpose of the Study:

To present a method for inferring minimal representations of spike train structure.
To characterize the complexity and randomness of neural firing patterns.

Main Methods:

Utilized information-theoretic analysis of prediction to infer causal state models (CSMs).
CSMs are minimal hidden Markov models representing the spike train generation process.
Employed the causal state splitting reconstruction algorithm for nonparametric inference.

Main Results:

Demonstrated that expected algorithmic information content can be decomposed into complexity, internal entropy rate, and residual noise.
CSMs objectively quantify generalizable structure and idiosyncratic randomness in spike trains.
Validated the approach with simulated and experimental rat barrel cortex data.

Conclusions:

The developed method provides a novel way to analyze spike train complexity beyond traditional measures.
CSMs offer a powerful tool for understanding the statistical properties and computational principles of neural coding.