Compression detects changes in spiking neural data from cortical lesions

The complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying statistics. These algorithms may be used to efficiently estimate a signal's Shannon entropy rate, a biologically relevant measure of the complexity of a signal. It is therefore natural to explore their effectiveness in the analysis of spiking neural data. Approach: This work uses the inverse compression ratio (ICR) to analyze recordings (Utah arrays) taken from motor cortex of animals performing reaching tasks three days before and three days after administering electrolytic lesions (Subject U: 4 lesions, H: 3). We calculate ICR with temporally-independent lossless compression (gzip) and temporally-dependent lossy compression (H.264, MPEG-2). Compression-based ICR was compared to single-neuron measures used to understand spiking data (average firing rates and Fano factor), as well as common dimensionality reduction techniques (principal component analysis and factor analysis). Main Results: ICR is able to significantly (Mann-Whitney U test, p<0.01) detect lesions with higher accuracy than single-neuron metrics, but not dimensionality reduction (ICR methods: 85.7%, single-neuron methods: 78.6%, dimensionality reduction: 100%). Additionally, statistical results on the same data show that ICR metrics remain more stable than single-neuron methods after lesion. The bitrate parameter of lossy compression algorithms is swept to better understand the effect of information rates and "optimal" compression on lesion detection performance. Simulated data shows that ICR is computationally advantageous. Significance: These results suggest that compression algorithms may be a useful tool to detect and better understand perturbations to the underlying structure of neural data. Information-theoretic analyses may complement techniques like dimensionality reduction and firing rate tuning as a convenient and useful tool to characterize neural data.