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- W4387476857 abstract "Recent studies show that, even in constant environments, the tuning of single neurons changes over time in a variety of brain regions. This representational drift has been suggested to be a consequence of continuous learning under noise, but its properties are still not fully understood. To uncover the underlying mechanism, we trained an artificial network on a simplified navigational task, inspired by the predictive coding literature. The network quickly reached a state of high performance, and many neurons exhibited spatial tuning. We then continued training the network and noticed that the activity became sparser with time. We observed vastly different time scales between the initial learning and the ensuing sparsification. We verified the generality of this phenomenon across tasks, learning algorithms, and parameters. This sparseness is a manifestation of the movement within the solution space - the networks drift until they reach a flat loss landscape. This is consistent with recent experimental results demonstrating that CA1 neurons increase sparseness with exposure to the same environment and become more spatially informative. We conclude that learning is divided into three overlapping phases: Fast familiarity with the environment, slow implicit regularization, and a steady state of null drift. The variability in drift dynamics opens the possibility of inferring learning algorithms from observations of drift statistics." @default.
- W4387476857 created "2023-10-11" @default.
- W4387476857 date "2023-10-10" @default.
- W4387476857 modified "2023-10-11" @default.
- W4387476857 title "Reviewer #2 (Public Review): Representational drift as a result of implicit regularization" @default.
- W4387476857 doi "https://doi.org/10.7554/elife.90069.1.sa2" @default.
- W4387476857 hasPublicationYear "2023" @default.
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