Sparse Distributed Memory is a Continual Learner

Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov, Gabriel Kreiman

Keywords: Sparse Distributed Memory, Sparsity, Top-K Activation, Continual Learning, Biologically Inspired

TL;DR: Improving Sparse Distributed Memory via additional neurobiology results in a deep learning model with strong, organic continual learning and insights into sparse models more broadly.

Abstract: Continual learning is a problem for artificial neural networks that their biological counterparts are adept at solving. Building on work using Sparse Distributed Memory (SDM) to connect a core neural circuit with the powerful Transformer model, we create a modified Multi-Layered Perceptron (MLP) that is a strong continual learner. We find that every component of our MLP variant translated from biology is necessary for continual learning. Our solution is also free from any memory replay or task information, and introduces novel methods to train sparse networks that may be broadly applicable.

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Please Choose The Closest Area That Your Submission Falls Into: Neuroscience and Cognitive Science (e.g., neural coding, brain-computer interfaces)

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:2303.11934/code)