Biology subjects

Nuyujukian, P.

Publications and source records attributed to Nuyujukian, P..

10 recordsLinked to original sources

bioRxiv2026

Stiefel Manifold Dynamical Systems for Tracking Representational Drift

Understanding neural dynamics is crucial for uncovering how the brain processes information and controls behavior. Linear dynamical systems (LDS) are widely used for modeling neural data due to their simplicity and effectiveness in capturing latent dynamics. However, LDS assumes a stable mapping from the latent states to neural activity, limiting its ability to capture representational drift--gradual changes in the brains representation of the external world. To address this, we introduce the Stiefel Manifold Dynamical System (SMDS), a new class of model designed to account for drift in neural representations across trials. In SMDS, emission matrices are constrained to be orthonormal and evolve smoothly over trials on the Stiefel manifold--the space of all orthonormal matrices--while the dynamics parameters are shared. This formulation allows SMDS to leverage data across trials while accounting for non-stationarity, thus capturing the underlying neural dynamics more accurately compared to an LDS. We apply SMDS to both simulated datasets and neural recordings across species. Our results consistently show that SMDS outperforms LDS in terms of log-likelihood and requires fewer latent dimensions to capture the same activity. Moreover, SMDS provides a powerful framework for quantifying and interpreting representational drift. It reveals a gradual drift over the course of minutes in the neural recordings and uncovers varying drift rates across dimensions, with slower drift in behaviorally and neurally significant dimensions.

neuroscience↗

bioRxiv2026

Stability of neural manifolds at minimal dimensionality despite motor representational drift

Recent work has modeled the generation of movement as emerging from a latent dynamical system. While the relationship between recorded neural populations and latent variables is non-stationary, latent trajectories populate low-dimensional manifolds that appear stable over time. However, the dimensionality of these manifolds and their relationship to motor cortical circuitry remains unclear. We propose a simple framework for extracting labeled latent variables that maintain a fixed relationship to movement parameters in a two-dimensional reaching task. Despite only capturing 3-7% of total variance and spanning two dimensions, this supervised method outperforms other common methods at an offline decoding task, and explains the long-term stability of neural manifolds observed in previous literature. We demonstrate that changes in the encoding map (from neurons to latents) is the dominant source of drift in motor cortex, while the latent map (from latents to behavior) remains stable over years. Additionally, we find that, for a two-dimensional task, neural structure is constrained by task complexity, limiting the insights that neural manifolds offer onto underlying circuitry. Our results motivate future studies to uncover causal relationships between neural computation and behavior.

neuroscience↗

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↗

bioRxiv2026

Neural modes in motor cortex cycle over fast timescales

The prevailing opinion in systems neuroscience is that motor cortex generates consistent behavior through a stable neural manifold--a low-dimensional mapping from neuron population activity to kinematics. However, standard population analyses group many repeated trials to reduce estimation noise, obscuring the time-varying activity of the network. Here, a block-by-block analysis of task trials in two species revealed that motor cortex rotates its principal subspaces as the contribution of individual neurons fluctuates, maintaining stable dynamics that are coupled to the neural state. While static decoders performed inconsistently due to non-stationary neuron activity, we confirmed that decoding performance could be rescued through subspace alignment, finding that roughly half of this geometric correction was driven by the internal rank-swapping of dominant state eigenvectors. Rather than relying on a static mapping to execute reaching behavior, motor cortex maintains a library of redundant state eigenvectors that generalize over different reach directions. Model simulations demonstrated that strong excitatory coupling combined with spike-rate adaptation is sufficient to produce a latent library whose elements can swap rank during continuous reparameterization of the network state. Together, these results refine our understanding of motor control: stable neural manifolds drift and jump within bounded limits, continuously outrunning neuron fatigue during repetitive activation.

neuroscience↗

bioRxiv2025

Context-dependent low-dimensional neural dynamics unfold in distinct subspaces, dimensionality, and dynamical strength for natural walking and reaching

Awake behaving animal experiments paired with multichannel electrode recordings have advanced motor systems neuroscience in creating models of how the mammalian brain controls move-ments. However, growing theoretical and experimental evidence question the generalizability of such findings from constrained studies to ambulatory behavior, highlighting a limitation in our understanding of how the brain controls movement. To address this question, spiking neural activity during highly-practiced, routine movement (walking) and goal-directed behavior (reach-ing towards food) were compared in an unconstrained setting. Kinematic trajectories of the contralateral arm during reaching and walking were statistically similar, as were the average single-neuron firing rates during these respective movements. However, the dimensionality of reaching was higher than that of walking and existed in largely non-overlapping subspaces. Further, when modeled as dynamical systems, reaching decayed 3-5 times more quickly than walking. Taken together, these findings demonstrate that the low-dimensional structure of motor cortex is more complex for goal-directed reaching than in highly-practiced natural movements. Since this difference is primarily observable at the state and dynamical systems level, these findings suggest behavioral context plays a significant role in the coordination of otherwise kinematically similar movements, providing indirect evidence for non-cortical circuits such as central pattern generators.

neuroscience↗

bioRxiv2025

Signatures of covert neuron loss in the local field potential of motor cortex

BackgroundCovert stroke is understudied despite occurring ten times for every symptomatic stroke and contributing to strokes enormous global disease burden. For instance, it is unknown whether covert stroke replicates the peri-infarct neuroelectrophysiological frequency spectrum impact of symptomatic stroke. This motivated the use of our novel electrolytic lesioning platform to explore the spectral consequences of covert neuron loss. MethodsDuring a multi-month period of participation in an arm reaching task, neuron loss was induced via electrolytic lesions to the motor cortex of two large animals (U: n = 4; H: n = 7). Behavioral metrics were paired with spectrum estimates from local field potential recordings. These spectra were represented as bandpowers, aperiodic/periodic parameters, and decomposed time-frequency tensors. Lesion impact was assessed using nonparametric permutation tests of next-day effects and state space model parameters. ResultsTask success rate was unaffected by lesions, but shifts in aperiodic structure reduced next-day{gamma} bandpower (30 - 100 Hz; U: -1.38{micro}V2, p < 1 x 10-3; H: -1.66{micro}V2, p = 0.001) while sensorimotor rhythms spanning 8 - 45 Hz ({Sigma}SMR) were amplified (Monkey U: 8.68{micro}V2, p < 1 x 10-3; Monkey H: 2.40{micro}V2, p = 0.004). Additionally, state space modeling showed that spectral perturbations to{gamma} and {Sigma}SMR outlasted any behavioral impact of the lesions (Monkey U: Behavior=1 d,{gamma} =2 d, {Sigma}SMR =2 d; Monkey H: Behavior=0 d,{gamma} =3 d, {Sigma}SMR =1 d). Finally, tensor decomposition revealed interpretable, animal-specific perturbations to time-frequency dynamics. ConclusionCovert neuron loss induced multi-day spectral perturbations similar to those observed after symptomatic stroke. The neural spectrum is thus more sensitive to neuron loss than previously understood and could be responsive to neuron loss caused by covert stroke. This work also motivates the use of electrolytic lesions to bridge covert and symptomatic regimes of neuron loss, advancing our causal understanding of post-stroke interactions between spectrum and behavior.

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↗

bioRxiv2022

Neuroelectrophysiology-Compatible Electrolytic Lesioning

Lesion studies have historically been instrumental for establishing causal connections between brain and behavior. They stand to provide additional insight if integrated with multielectrode techniques common in systems neuroscience. Here we present and test a platform for creating electrolytic lesions through chronically implanted, intracortical multielectrode probes without compromising the ability to acquire neuroelectrophysiology. A custom-built current source provides stable current and allows for controlled, repeatable lesions in awake-behaving animals. Performance of this novel lesioning technique was validated using histology from ex vivo and in vivo testing, current and voltage traces from the device, and measurements of spiking activity before and after lesioning. This electrolytic lesioning method avoids disruptive procedures, provides millimeter precision over the extent and submillimeter precision over the location of the injury, and permits electrophysiological recording of single-unit activity from the remaining neuronal population after lesioning. This technique can be used in many areas of cortex, in several species, and theoretically with any multielectrode probe. The low-cost, external lesioning device can also easily be adopted into an existing electrophysiology recording setup. This technique is expected to enable future causal investigations of the recorded neuronal populations role in neuronal circuit function, while simultaneously providing new insight into local reorganization after neuron loss.

neuroscience↗

bioRxiv2019

Decoding and perturbing decision states in real time

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment. The process of deliberation on the evidence can be described by a time-varying decision variable (DV), decoded from neural activity, that predicts a subjects decision at the end of a trial. However, within trials, large moment-to-moment fluctuations of the DV are observed. The behavioral significance of these fluctuations and their role in the decision process remain unclear. Here we show that within-trial DV fluctuations decoded in real time from motor cortex are tightly linked to choice behavior, and that robust changes in DV sign have the statistical regularities expected from behavioral studies of changes-of-mind. Furthermore, we find single-trial evidence for absorbing decision bounds. As the DV builds up, heavily favoring one or the other choice, moment-to-moment variability in the DV is reduced, and both neural DV and behavioral decisions become resistant to additional pulses of sensory evidence as predicted by diffusion-to-bound and attractor models of the decision process.

neuroscience↗