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• W2969548943 startingPage "e1006604" @default.
• W2969548943 abstract "We provide a novel computational framework on how biological and artificial agents can learn to flexibly couple and decouple neural task modules for cognitive processing. In this way, they can address the stability-plasticity dilemma. For this purpose, we combine two prominent computational neuroscience principles, namely Binding by Synchrony and Reinforcement Learning. The model learns to synchronize task-relevant modules, while also learning to desynchronize currently task-irrelevant modules. As a result, old (but currently task-irrelevant) information is protected from overwriting (stability) while new information can be learned quickly in currently task-relevant modules (plasticity). We combine learning to synchronize with task modules that learn via one of several classical learning algorithms (Rescorla-Wagner, backpropagation, Boltzmann machines). The resulting combined model is tested on a reversal learning paradigm where it must learn to switch between three different task rules. We demonstrate that our combined model has significant computational advantages over the original network without synchrony, in terms of both stability and plasticity. Importantly, the resulting models’ processing dynamics are also consistent with empirical data and provide empirically testable hypotheses for future MEG/EEG studies." @default.
• W2969548943 created "2019-08-29" @default.
• W2969548943 creator A5041836424 @default.
• W2969548943 creator A5054766869 @default.
• W2969548943 date "2019-08-20" @default.
• W2969548943 modified "2023-10-14" @default.
• W2969548943 title "Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma" @default.
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• W2969548943 doi "https://doi.org/10.1371/journal.pcbi.1006604" @default.
• W2969548943 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6716678" @default.
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• W2969548943 hasPublicationYear "2019" @default.
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