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A long-term goal of AI is to produce agents that can learn a diversity ofskills throughout their lifetimes and continuously improve those skills viaexperience. A longstanding obstacle towards that goal is catastrophicforgetting, which is when learning new information erases previously learnedinformation. Catastrophic forgetting occurs in artificial neural networks(ANNs), which have fueled most recent advances in AI. A recent paper proposedthat catastrophic forgetting in ANNs can be reduced by promoting modularity,which can limit forgetting by isolating task information to specific clustersof nodes and connections (functional modules). While the prior work did showthat modular ANNs suffered less from catastrophic forgetting, it was not ableto produce ANNs that possessed task-specific functional modules, therebyleaving the main theory regarding modularity and forgetting untested. Weintroduce diffusion-based neuromodulation, which simulates the release ofdiffusing, neuromodulatory chemicals within an ANN that can modulate (i.e. upor down regulate) learning in a spatial region. On the simple diagnosticproblem from the prior work, diffusion-based neuromodulation 1) inducestask-specific learning in groups of nodes and connections (task-specificlocalized learning), which 2) produces functional modules for each subtask, and3) yields higher performance by eliminating catastrophic forgetting. Overall,our results suggest that diffusion-based neuromodulation promotes task-specificlocalized learning and functional modularity, which can help solve thechallenging, but important problem of catastrophic forgetting.

Diffusion-based neuromodulation can eliminate catastrophic forgetting in simple neural networks