ReviewNeuron2021
How learning unfolds in the brain: toward an optimization view.
Review in Neuron, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- Manifold-constrained plasticity enables stable learning in recurrent neural circuits.PLoS computational biology · 2026Article
- But What Behavior?ArXiv · 2026Article
- Complementary roles of cell-type-specific plasticity in shaping neocortical dynamics for learning action timing.Nature communications · 2026Article
- Brain-computer interfaces as a causal probe for scientific inquiry.Trends in cognitive sciences · 2026Review
- Fluctuation-learning relationship in recurrent neural networks.Nature communications · 2025Article
- De novo sensorimotor learning through reuse of movement components.PLoS computational biology · 2024Article
- Vagus nerve stimulation during training fails to improve learning in healthy rats.Scientific reports · 2024Article
- Review
- Dimensionality reduction beyond neural subspaces with slice tensor component analysis.Nature neuroscience · 2024Article
- Learning leaves a memory trace in motor cortex.Current biology : CB · 2024Article
- Developmental changes in exploration resemble stochastic optimization.Nature human behaviour · 2023Article
- Distinguishing Learning Rules with Brain Machine Interfaces.Advances in neural information processing systems · 2022Article
- Heksor: the central nervous system substrate of an adaptive behaviour.The Journal of physiology · 2022Article
- Editorial: Integrating multimodal approaches to unravel neural mechanisms of learning and cognition.Frontiers in neurologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
How do changes in the brain lead to learning? To answer this question, consider an artificial neural network (ANN), where learning proceeds by optimizing a given objective or cost function. This "optimization framework" may provide new insights into how the brain learns, as many idiosyncratic features of neural activity can be recapitulated by an ANN trained to perform the same task. Nevertheless, there are key features of how neural population activity changes throughout learning that cannot be readily explained in terms of optimization and are not typically features of ANNs. Here we detail three of these features: (1) the inflexibility of neural variability throughout learning, (2) the use of multiple learning processes even during simple tasks, and (3) the presence of large task-nonspecific activity changes. We propose that understanding the role of these features in the brain will be key to describing biological learning using an optimization framework.
Indexed as
Identifiers
What OpenQuestion holds
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.