Metadata only
Date
2023Type
- Conference Poster
ETH Bibliography
yes
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Abstract
Humans have the ability to quickly adapt to new tasks and generalise what they learned to improve the learning process itself. At the core of this capacity for meta-learning lies a difficult credit assignment problem. After a learning episode, the system needs to determine how to change the components of plasticity such that the next time a similar task is encountered, a better learning outcome can be arrived at more quickly. In machine learning, this problem is often approached by storing the entire learning trajectory and revisiting it in reverse-time order, a process that places large demands on memory and is non-local in time. Here, we propose that hippocampal replay enables meta-learning in the neocortex without storing the entire learning trajectory. Show more
Publication status
publishedExternal links
Publisher
Science Communications World WideEvent
Organisational unit
03672 - Steger, Angelika / Steger, Angelika
Notes
Poster presentation on March 10, 2023More
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ETH Bibliography
yes
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