ArticlePLoS computational biology2016
Neural Population Dynamics during Reaching Are Better Explained by a Dynamical System than Representational Tuning.
Article in PLoS computational biology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers.
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Who cites it
92 citing papers in PubMed.
- Flexible reorientation of conserved neural dynamics underlies grasp control.bioRxiv : the preprint server for biology · 2026Article
- Sparse component analysis: A method that uncovers separable computations within neural population activity.Neuron · 2026Article
- Limb state accounts for differences between motor imagery and action in motor cortex.medRxiv : the preprint server for health sciences · 2026Article
- The equilibrium point hypothesis revisited: why threshold control does not explain human movement.Experimental brain research · 2026Review
- Shared somatosensory-motor neural population dynamics track motor recovery after stroke.bioRxiv : the preprint server for biology · 2026Article
- Insights into motor control: predict muscle activity from upper limb kinematics with LSTM networks.Scientific reports · 2026Article
- Switching between Newly Learned Motor Skills.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Article
- 3D directional tuning in the orofacial sensorimotor cortex during natural feeding and drinking.eLife · 2025Article
- Neural trajectories improve motor precision.bioRxiv : the preprint server for biology · 2025Article
- Beta-band desynchronization in the human hippocampus during movement preparation in a delayed reach task.Experimental brain research · 2025Article
- High-dimensional neuronal activity from low-dimensional latent dynamics: a solvable model.bioRxiv : the preprint server for biology · 2025Article
- Neural geometry from mixed sensorimotor selectivity for predictive sensorimotor control.eLife · 2025Article
- 3D directional tuning in the orofacial sensorimotor cortex during natural feeding and drinking.bioRxiv : the preprint server for biology · 2025Article
- Hand position fields of neurons in the premotor cortex of macaques during natural reaching.Nature communications · 2025Article
- Neural heterogeneity enhances reliable neural information processing: Local sensitivity and globally input-slaved transient dynamics.Science advances · 2025Article
- Next-Generation Neurotechnologies Inspired by Motor Primitive Model for Restoring Human Natural Movement.Research (Washington, D.C.) · 2025Review
- Review
- Motor Cortex Latent Dynamics Encode Spatial and Temporal Arm Movement Parameters Independently.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Reach-dependent reorientation of rotational dynamics in motor cortex.Nature communications · 2024Article
- Article
32 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Recent models of movement generation in motor cortex have sought to explain neural activity not as a function of movement parameters, known as representational models, but as a dynamical system acting at the level of the population. Despite evidence supporting this framework, the evaluation of representational models and their integration with dynamical systems is incomplete in the literature. Using a representational velocity-tuning based simulation of center-out reaching, we show that incorporating variable latency offsets between neural activity and kinematics is sufficient to generate rotational dynamics at the level of neural populations, a phenomenon observed in motor cortex. However, we developed a covariance-matched permutation test (CMPT) that reassigns neural data between task conditions independently for each neuron while maintaining overall neuron-to-neuron relationships, revealing that rotations based on the representational model did not uniquely depend on the underlying condition structure. In contrast, rotations based on either a dynamical model or motor cortex data depend on this relationship, providing evidence that the dynamical model more readily explains motor cortex activity. Importantly, implementing a recurrent neural network we demonstrate that both representational tuning properties and rotational dynamics emerge, providing evidence that a dynamical system can reproduce previous findings of representational tuning. Finally, using motor cortex data in combination with the CMPT, we show that results based on small numbers of neurons or conditions should be interpreted cautiously, potentially informing future experimental design. Together, our findings reinforce the view that representational models lack the explanatory power to describe complex aspects of single neuron and population level activity.
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