Attention Approximates Sparse Distributed Memory

While Attention has come to be an important mechanism in deep learning, there remains limited intuition for why it works so well. Here, we show that Transformer Attention can be closely related under certain data conditions to Kanerva's Sparse Distributed Memory (SDM), a biologically plausible associative memory model. We confirm that these conditions are satisfied in pre-trained GPT2 Transformer models. We discuss the implications of the Attention-SDM map and provide new computational and biological interpretations of Attention. 1 This cerebellar relationship is additionally compelling by the fact that cerebellum-like neuroanatomy exists in many other organisms including numerous insects (eg. the Drosophila Mushroom Body) and potentially cephalopods [17, 18, 19, 20, 21] . 35th Conference on Neural Information Processing Systems (NeurIPS 2021).