Biology subjects

Abdulla, M. U.

Publications and source records attributed to Abdulla, M. U..

3 recordsLinked to original sources

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

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↗