Oct 30, 2018

Learning to synchronize: How biological agents can couple neural task modules for dealing with the stability-plasticity dilemma

BioRxiv : the Preprint Server for Biology
Pieter Verbeke, Tom Verguts


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 several classical learning algorithms (backpropagation, Boltzmann machines, Rescorla-Wagner). For each case, we demonstrate that our combined model has significant computational advantages over the original network in 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.

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Mentioned in this Paper

Metal Working Fluid
Neuronal Plasticity
Psychological Reinforcement
Pharmacologic Substance
Binding (Molecular Function)
Enzyme Stability
Biological Factors

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