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Compression detects changes in spiking neural data from cortical lesions
Alice Tor1, Yuxin Wu1, Stephen E Clarke2,3
1Electrical Engineering Department, Stanford University, Stanford, CA, United States of America.
Journal of Neural Engineering
|March 20, 2026
Summary
Universal compression algorithms effectively analyze neural data complexity. Inverse compression ratio (ICR) detects brain lesions with high accuracy, outperforming single-neuron metrics and offering a novel tool for neuroscience research.
Area of Science:
- Neuroscience
- Information Theory
- Computational Biology
Background:
- Neural data complexity fluctuates during information processing.
- Universal compression algorithms exploit data redundancies for near-optimal compression.
- These algorithms can estimate Shannon entropy rate, a measure of signal complexity.
Purpose of the Study:
- To explore the effectiveness of universal compression algorithms in analyzing spiking neural data.
- To investigate the use of Inverse Compression Ratio (ICR) for detecting changes in neural data complexity.
- To compare compression-based metrics with traditional methods for neural data analysis.
Main Methods:
- Utilized Inverse Compression Ratio (ICR) on Utah array recordings from motor cortex.
- Analyzed neural data from animals performing reaching tasks before and after electrolytic lesions.
- Calculated ICR using lossless (gzip) and lossy (H.264, MPEG-2) compression algorithms.
- Compared ICR with single-neuron metrics (firing rates, Fano factor) and dimensionality reduction techniques (PCA, factor analysis).
Main Results:
- ICR significantly detected lesions with higher accuracy (85.7%) than single-neuron metrics (78.6%).
- Dimensionality reduction techniques achieved 100% accuracy in lesion detection.
- ICR metrics demonstrated greater stability than single-neuron methods post-lesion.
- Simulated data indicated ICR's computational advantages.
Conclusions:
- Compression algorithms, particularly ICR, show promise as tools for detecting and understanding perturbations in neural data structure.
- Information-theoretic analyses can complement existing techniques like dimensionality reduction and firing rate tuning for neural data characterization.
