ReviewTrends in neurosciences2023
Continual task learning in natural and artificial agents.
Review in Trends in neurosciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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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.
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Who cites it
17 citing papers in PubMed.
- Contextual inference through flexible integration of environmental features and behavioural outcomes.PLoS computational biology · 2026Article
- Domain-specific schema reuse supports flexible learning to learn in the primate brain.Nature communications · 2026Article
- The effects of task similarity during representation learning in brains and neural networks.Nature communications · 2025Article
- Factorized embedding of goal and uncertainty in the lateral prefrontal cortex guides stably flexible learning.Nature communications · 2025Article
- Article
- Semantic Anchors Facilitate Task Encoding in Continual Learning.Open mind : discoveries in cognitive science · 2025Article
- Cerebellar-driven cortical dynamics can enable task acquisition, switching and consolidation.Nature communications · 2024Article
- Dynamics of Supervised and Reinforcement Learning in the Non-Linear Perceptron.Advances in neural information processing systems · 2024Article
- Rapid context inference in a thalamocortical model using recurrent neural networks.Nature communications · 2024Article
- General knowledge and detailed memory benefit from different training sequences.Journal of applied research in memory and cognition · 2024Article
- A framework for the emergence and analysis of language in social learning agents.Nature communications · 2024Article
- Reconciling shared versus context-specific information in a neural network model of latent causes.Scientific reports · 2024Article
- Memory Reactivation during Sleep Does Not Act Holistically on Object Memory.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2024Article
- Paradoxical replay can protect contextual task representations from destructive interference when experience is unbalanced.bioRxiv : the preprint server for biology · 2024Article
- Blocked training facilitates learning of multiple schemas.Communications psychology · 2024Article
- Memory reactivation during sleep does not act holistically on object memory.bioRxiv : the preprint server for biology · 2024Article
- Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents.Frontiers in computational neuroscience · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
How do humans and other animals learn new tasks? A wave of brain recording studies has investigated how neural representations change during task learning, with a focus on how tasks can be acquired and coded in ways that minimise mutual interference. We review recent work that has explored the geometry and dimensionality of neural task representations in neocortex, and computational models that have exploited these findings to understand how the brain may partition knowledge between tasks. We discuss how ideas from machine learning, including those that combine supervised and unsupervised learning, are helping neuroscientists understand how natural tasks are learned and coded in biological brains.
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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.