Flesch, Timo;
Saxe, Andrew;
Summerfield, Christopher;
(2023)
Continual task learning in natural and artificial agents.
Trends in Neurosciences
10.1016/j.tins.2022.12.006.
(In press).
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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.
| Type: | Article |
|---|---|
| Title: | Continual task learning in natural and artificial agents |
| Location: | England |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.tins.2022.12.006 |
| Publisher version: | https://doi.org/10.1016/j.tins.2022.12.006 |
| Language: | English |
| Additional information: | © 2022 The Authors. Published by Elsevier Ltd. under a Creative Commons license (https://creativecommons.org/licenses/by/4.0/). |
| Keywords: | Hebbian gating, machine learning, neural networks, neuroimaging, representational geometry |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences > Gatsby Computational Neurosci Unit |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164078 |
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