Neural signatures of motor memories emerge in
neural network models
neural network models
File(s)
Reviewed Preprint (v.1)
Author(s)
Chang, Joanna
Clopath, Claudia
Gallego, Juan Alvaro
Type
Journal Article
Abstract
Animals can learn and seamlessly perform a great number of behaviors. However, it is unclear how neural activity can accommodate new behaviors without interfering with those an animal has already acquired. Recent studies in monkeys performing motor and brain-computer interface (BCI) learning tasks have identified neural signatures—so-called “memory traces” and “uniform shifts”—that appear in the neural activity of a familiar task after learning a new task. Here we asked when these signatures arise and how they are related to continual learning. By modeling a BCI learning paradigm, we show that both signatures emerge naturally as a consequence of learning, without requiring a specific mechanism. In general, memory traces and uniform shifts reflected savings by capturing how information from different tasks coexisted in the same neural activity patterns. Yet, although the properties of these two different signatures were both indicative of savings, they were uncorrelated with each other. When we added contextual inputs that separated the activity for the different tasks, these signatures decreased even when savings were maintained, demonstrating the challenges of defining a clear relationship between neural activity changes and continual learning.
Date Issued
2025-08-27
Date Acceptance
2025-03-20
Citation
eLife, 2025
ISSN
2050-084X
Publisher
eLife Sciences Publications Ltd
Journal / Book Title
eLife
Copyright Statement
© 2025, Chang et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
License URL
Identifier
10.7554/eLife.106871.1
Publication Status
Published online
Date Publish Online
2025-08-27