Biology subjects

Silvernagel, M. P.

Publications and source records attributed to Silvernagel, M. P..

3 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

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

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↗