Modelling continual learning in humans with Hebbian context gating and exponentially decaying task signals

Humans can learn several tasks in succession with minimal mutual interference but perform more poorly when trained on multiple tasks at once. The opposite is true for standard deep neural networks. Here, we propose novel computational constraints for artificial neural networks, inspired by earlier w...

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Bibliographic Details
Main Authors: Timo Flesch, David G. Nagy, Andrew Saxe, Christopher Summerfield
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2023-01-01
Series:PLoS Computational Biology
Online Access:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9851563/?tool=EBI