Biology subjects

Tor, A.

Publications and source records attributed to Tor, A..

3 recordsLinked to original sources

bioRxiv2026

Compression Detects Changes in Spiking Neural Data from Cortical Lesions

1ObjectiveThe 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 conveniently and efficiently estimate a given signals 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. ApproachThis work focuses on using compression to analyze recordings (96-channel Utah arrays) taken from motor cortex of animals performing reaching tasks for three days before and three days after administering electrolytic lesions (Subject U: 4 lesions, H: 3). In particular, we use the inverse compression ratio (ICR), which compares the sizes of compressed and uncompressed data to estimate the amount of statistically unique information. 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, such as average firing rates and Fano factor. Compression is also compared to common dimensionality reduction techniques, principal component analysis (PCA) and factor analysis (FA). Main ResultsStatistical tests on aggregate data comparing each metric before and after lesioning reveal that 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. Our conclusions are confirmed by the same analyses performed on several different simulated neural datasets. SignificanceThese 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.

neuroscience↗

bioRxiv2026

The curse of dimensionality in motor cortex

Understanding how motor cortex generates movement is a foundational challenge in neuroscience. Unsupervised dimensionality reduction techniques, such as principal component analysis (PCA), are widely used to transform high-dimensional neural recordings into a compact, low-dimensional space. The dimensionality of this space--that is, the number of principal components needed to explain a fixed fraction of variance--is broadly assumed to be an intrinsic property of the underlying neural dynamics, potentially modulated by task complexity. Here, by comparing con-strained reaching and unconstrained naturalistic behaviors recorded from the same animal on the same day, we show that this assumption breaks down in two distinct ways. First, across four non-human primates, the dominant axes of low-dimensional neural activity separate behavioral contexts rather than movement kinematics, with neural activity shifting rapidly between task-specific regions of state space at task transitions. Notably, traditional dimensionality metrics are insensitive to movement complexity across tasks. Instead, unsupervised dimensionality scales with the number of recorded neurons, exhibiting non-saturating growth up to 1000 simultaneously recorded electrodes, a pattern that holds across PCA, factor analysis, shared variance component analysis, and nonlinear autoencoders. This scaling has direct consequences for decoding: while decoders trained on unsupervised subspaces improve only modestly with electrode count, super-vised methods leverage additional electrodes to separate neural states from a vanishingly small fraction of total variance (<10% at 1000 electrodes). Together, these results challenge current views on cortical dimensionality, reveal a greater-than-appreciated role for behavioral context in shaping motor cortical activity, and motivate careful consideration of computational methods as experimental data volumes scale.

neuroscience↗

bioRxiv2025

Material Damage to Multielectrode Arrays after Electrolytic Lesioning is in the Noise

The quality of stable long-term recordings from chronically implanted electrode arrays is essential for experimental neu-roscience and brain-computer interfaces. This work uses scanning electron microscopy (SEM) to image and analyze eight 96-channel Utah arrays previously implanted in motor cortical regions of four subjects (subject H = 2242 days implanted, F = 1875, U = 2680, C = 594), providing important contributions to a growing body of long-term implant research leveraging this imaging technology. Four of these arrays have been used in electrolytic lesioning experiments (H = 10 lesions, F = 1, U = 4, C = 1), a novel electrolytic perturbation technique using small direct currents. In addition to surveying physical damage, such as biological debris and material deterioration, this work also analyzes whether electrolytic lesioning created damage beyond what is typical for these arrays. These findings also indicate that there are no statistically significant differences between the damage observed on normal electrodes versus electrodes used for electrolytic lesioning, providing evidence that electrolytic lesioning does not significantly affect the quality of chronically implanted electrode arrays. Finally, this work also includes the largest collection of single-electrode SEM images for previously implanted multielectrode Utah arrays, spanning eleven different intact arrays and one broken array. As the clinical relevance of chronically implanted electrodes with single-neuron resolution continues to grow, these images may be used to provide the foundation for a larger public database and inform further electrode design and analyses.

neuroscience↗