ArticlePLoS computational biology2021
Neural manifold under plasticity in a goal driven learning behaviour.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Human learning of noninvasive brain-computer interfaces via manifold geometry.Nature neuroscience · 2026Article
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- Fluctuation-learning relationship in recurrent neural networks.Nature communications · 2025Article
- Neuroscience of Behavior.NeuroSci · 2025Review
- A neural manifold view of the brain.Nature neuroscience · 2025Review
- Metaplasticity and continual learning: mechanisms subserving brain computer interface proficiency.Journal of neural engineering · 2025Article
- Mechanistic insights into the P-coumaric acid protection against bisphenol A-induced hepatotoxicity in in vivo and in silico models.Scientific reports · 2025Article
- A neural implementation model of feedback-based motor learning.Nature communications · 2025Article
- A theory of brain-computer interface learning via low-dimensional control.bioRxiv : the preprint server for biology · 2025Article
- De novo motor learning creates structure in neural activity that shapes adaptation.Nature communications · 2024Article
- Nonlinear manifolds underlie neural population activity during behaviour.bioRxiv : the preprint server for biology · 2024Article
- Article
- Reconstructing computational system dynamics from neural data with recurrent neural networks.Nature reviews. Neuroscience · 2023Review
- Inter-areal transmission of multiple neural signals through frequency-division-multiplexing communication.Cognitive neurodynamics · 2023Article
- A unifying perspective on neural manifolds and circuits for cognition.Nature reviews. Neuroscience · 2023Review
- Article
- Aligning latent representations of neural activity.Nature biomedical engineering · 2023Article
- Neural manifold analysis of brain circuit dynamics in health and disease.Journal of computational neuroscience · 2023Review
- Distinguishing Learning Rules with Brain Machine Interfaces.Advances in neural information processing systems · 2022Article
- Small, correlated changes in synaptic connectivity may facilitate rapid motor learning.Nature communications · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Neural activity is often low dimensional and dominated by only a few prominent neural covariation patterns. It has been hypothesised that these covariation patterns could form the building blocks used for fast and flexible motor control. Supporting this idea, recent experiments have shown that monkeys can learn to adapt their neural activity in motor cortex on a timescale of minutes, given that the change lies within the original low-dimensional subspace, also called neural manifold. However, the neural mechanism underlying this within-manifold adaptation remains unknown. Here, we show in a computational model that modification of recurrent weights, driven by a learned feedback signal, can account for the observed behavioural difference between within- and outside-manifold learning. Our findings give a new perspective, showing that recurrent weight changes do not necessarily lead to change in the neural manifold. On the contrary, successful learning is naturally constrained to a common subspace.
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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.