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Review
. 2022 Sep;45(9):656-666.
doi: 10.1016/j.tins.2022.06.002. Epub 2022 Jul 4.

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem

Peter Jedlicka  1 , Matus Tomko  2 , Anthony Robins  3 , Wickliffe C Abraham  4
Affiliations
Review

Contributions by metaplasticity to solving the Catastrophic Forgetting Problem

Peter Jedlicka et al. Trends Neurosci. 2022 Sep.

Erratum in

Abstract

Catastrophic forgetting (CF) refers to the sudden and severe loss of prior information in learning systems when acquiring new information. CF has been an Achilles heel of standard artificial neural networks (ANNs) when learning multiple tasks sequentially. The brain, by contrast, has solved this problem during evolution. Modellers now use a variety of strategies to overcome CF, many of which have parallels to cellular and circuit functions in the brain. One common strategy, based on metaplasticity phenomena, controls the future rate of change at key connections to help retain previously learned information. However, the metaplasticity properties so far used are only a subset of those existing in neurobiology. We propose that as models become more sophisticated, there could be value in drawing on a richer set of metaplasticity rules, especially when promoting continual learning in agents moving about the environment.

Keywords: artificial neural network; continual learning; deep learning; learning rate; memory retention; synaptic plasticity.

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Conflict of interest statement

Declaration of interests The authors have no interests to declare.

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