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- W4386638430 endingPage "102759" @default.
- W4386638430 startingPage "102759" @default.
- W4386638430 abstract "While neural plasticity has long been studied as the basis of learning, the growth of large-scale neural recording techniques provides a unique opportunity to study how learning-induced activity changes are coordinated across neurons within the same circuit. These distributed changes can be understood through an evolution of the geometry of neural manifolds and latent dynamics underlying new computations. In parallel, studies of multi-task and continual learning in artificial neural networks hint at a tradeoff between non-interference and compositionality as guiding principles to understand how neural circuits flexibly support multiple behaviors. In this review, we highlight recent findings from both biological and artificial circuits that together form a new framework for understanding task learning at the population level." @default.
- W4386638430 created "2023-09-13" @default.
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- W4386638430 date "2023-12-01" @default.
- W4386638430 modified "2023-10-14" @default.
- W4386638430 title "Signatures of task learning in neural representations" @default.
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- W4386638430 doi "https://doi.org/10.1016/j.conb.2023.102759" @default.
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