Modulating interactions to control dynamics of neural networks
Keywords: associative memory, sequence generation, synaptic modulation, nonlinear control
TL;DR: We introduce "interaction modulation" as a new class of models for retrieving sequential memory in neural networks.
Abstract: Sequential retrieval of stored patterns is a fundamental task that can be performed by neural networks. Previous models of sequential retrieval belong to a general class in which the components of the network are controlled by a slow feedback ("input modulation"). In contrast, we introduce a new class of models in which the feedback modifies the interactions among the components ("interaction modulation"). In particular, we study a model in which the symmetric interactions are modulated. We show that this model is not only capable of retrieving dynamic sequences, but it does so more robustly than a canonical model of input modulation. Our model allows retrieval of patterns with different activity levels, is robust to feedback noise, and has a large dynamic capacity. Our results suggest that interaction modulation may be a new paradigm for controlling network dynamics.
Submission Number: 4
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