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

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.Author summary: Artificial and biological agents alike face a critical trade-off between being sufficiently adaptive to acquiring novel information (plasticity) and retaining older information (stability); this is known as the stability-plasticity dilemma. Previous work on this dilemma has focused either on computationally efficient solutions for artificial agents or on biologically plausible frameworks for biological agents. What is lacking is a solution that is both computationally efficient and empirically testable on biological agents. Therefore, the current work proposes a computational framework on the stability-plasticity dilemma that provides empirically testable hypotheses on both neural and behavioral levels. In this framework, neural task modules can be flexibly coupled and decoupled depending on the task at hand. Testing this framework will allow us to gain more insight in how biological agents deal with the stability-plasticity dilemma.