ArticleeLife2024
When and why does motor preparation arise in recurrent neural network models of motor control?
Article in eLife, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Distinct roles of brain network flexibility in motor learning across age.Imaging neuroscience (Cambridge, Mass.) · 2026Article
- Control Principles of Neural Dynamics Revealed by the Neurobiology of Timing.Annual review of neuroscience · 2025Review
- Continuous sensorimotor transformation enhances robustness of neural dynamics to perturbation in macaque motor cortex.Nature communications · 2025Article
- Cerebellar output shapes cortical preparatory activity during motor adaptation.Nature communications · 2025Article
- Cerebellar output shapes cortical preparatory activity during motor adaptation.bioRxiv : the preprint server for biology · 2025Article
- Dynamical constraints on neural population activity.Nature neuroscience · 2025Article
- Article
- Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics.Science advances · 2024Article
- Article
- When preparation pays off.eLife · 2024Article
- Reaching into the future.eLife · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
During delayed ballistic reaches, motor areas consistently display movement-specific activity patterns prior to movement onset. It is unclear why these patterns arise: while they have been proposed to seed an initial neural state from which the movement unfolds, recent experiments have uncovered the presence and necessity of ongoing inputs during movement, which may lessen the need for careful initialization. Here, we modeled the motor cortex as an input-driven dynamical system, and we asked what the optimal way to control this system to perform fast delayed reaches is. We find that delay-period inputs consistently arise in an optimally controlled model of M1. By studying a variety of network architectures, we could dissect and predict the situations in which it is beneficial for a network to prepare. Finally, we show that optimal input-driven control of neural dynamics gives rise to multiple phases of preparation during reach sequences, providing a novel explanation for experimentally observed features of monkey M1 activity in double reaching.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.