ENCORE: A Biologically Plausible Encoding Optimization for Online Associative Memory

Episodic memory requires rapid, one-shot encoding of experiences as they arrive, yet associative memory models such as Hopfield network and their modern variants (Modern Hopfield Network) are typically formulated and tested in the batch setting, where all patterns are stored simultaneously. We study the online associative memory problem, in which patterns arrive sequentially and must be encoded via one-shot exposure. Prior works have improved capacity along two axes: retrieval dynamics (e.g., Modern Hopfield Network) and learning rules (e.g., Storkey rule). Inspired by the hippocampus architecture and the neuronal memory allocation mechanism in the brain, we explore a third, orthogonal axis: optimizing the encoded patterns themselves. We propose an associative memory system in which the encoding dynamics are explicitly designed to minimized interference with previously stored encoded patterns. On online associative memory benchmarks, our system outperforms both Modern Hopfield networks and Hopfield network with Storkey learning rule by more than 5x in MNIST and 2x in ImageNet 128x128. These results suggest that pattern encoding, long overlooked relative to retrieval dynamics and learning rules, is a significant lever for designing associative memory in realistic online settings.