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Compression detects changes in spiking neural data from cortical lesions.

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Compression Detects Changes in Spiking Neural Data from Cortical Lesions.

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    Compression algorithms effectively detect neural data changes after lesions, offering a stable alternative to single-neuron metrics. This information-theoretic approach complements existing methods for analyzing neural complexity.

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    Area of Science:

    • Neuroscience
    • Information Theory
    • Data Compression

    Background:

    • Neural data complexity evolves during information processing.
    • Universal compression algorithms estimate signal entropy rate, a measure of neural complexity.
    • Compression's utility in analyzing neural data remains largely unexplored.

    Purpose of the Study:

    • To investigate the effectiveness of compression algorithms in analyzing neural data.
    • To assess if compression-based metrics can detect changes in neural data complexity following lesions.
    • To compare compression-based analysis with traditional methods like single-neuron metrics and dimensionality reduction.

    Main Methods:

    • Analysis of 96-channel Utah array recordings from motor cortex during reaching tasks.
    • Application of lossless (gzip) and lossy (H.264, MPEG-2) compression to estimate Inverse Compression Ratio (ICR).
    • Comparison of ICR with average firing rates, Fano factor, Principal Component Analysis (PCA), and Factor Analysis (FA) before and after electrolytic lesions.

    Main Results:

    • ICR significantly detected lesions with higher accuracy (85.7%) than single-neuron metrics (78.6%), though less than dimensionality reduction (100%).
    • ICR metrics demonstrated greater stability than single-neuron metrics post-lesion.
    • Analyses on simulated neural datasets confirmed these findings.

    Conclusions:

    • Compression algorithms show promise as a tool for detecting and understanding structural perturbations in neural data.
    • Information-theoretic analyses, using compression, can complement existing techniques for neural data characterization.