Fast adaptation to rule switching using neuronal surprise.

Autor: Martin L L R Barry, Wulfram Gerstner
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: PLoS Computational Biology, Vol 20, Iss 2, p e1011839 (2024)
Druh dokumentu: article
ISSN: 1553-734X
1553-7358
DOI: 10.1371/journal.pcbi.1011839&type=printable
Popis: In humans and animals, surprise is a physiological reaction to an unexpected event, but how surprise can be linked to plausible models of neuronal activity is an open problem. We propose a self-supervised spiking neural network model where a surprise signal is extracted from an increase in neural activity after an imbalance of excitation and inhibition. The surprise signal modulates synaptic plasticity via a three-factor learning rule which increases plasticity at moments of surprise. The surprise signal remains small when transitions between sensory events follow a previously learned rule but increases immediately after rule switching. In a spiking network with several modules, previously learned rules are protected against overwriting, as long as the number of modules is larger than the total number of rules-making a step towards solving the stability-plasticity dilemma in neuroscience. Our model relates the subjective notion of surprise to specific predictions on the circuit level.
Databáze: Directory of Open Access Journals
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