A Biologically Plausible Dense Associative Memory with Exponential Capacity

ICLR 2026 Conference Submission20339 Authors

Keywords: Dense Associative Memory, Hopfield Network

TL;DR: We introduce a biologically plausible Dense Associative Memory model with exponential storage capacity.

Abstract: Krotov and Hopfield (2021) proposed a biologically plausible two-layer associative memory network with memory storage capacity exponential in the number of visible neurons. However, the capacity was only linear in the number of hidden neurons. This limitation arose from the choice of nonlinearity between the visible and hidden units, which enforced winner-takes-all dynamics in the hidden layer, thereby restricting each hidden unit to encode only a single memory. We overcome this limitation by introducing a novel associative memory network with a threshold nonlinearity that enables distributed representations. In contrast to winner-takes-all dynamics, where each hidden neuron is tied to an entire memory, our network allows hidden neurons to encode basic components shared across many memories. Consequently, complex patterns are represented through combinations of hidden neurons. These representations reduce redundancy and allow many correlated memories to be stored compositionally. Thus, we achieve much higher capacity: exponential in the number of hidden units, provided the number of visible units is sufficiently larger than the number of hidden neurons. Exponential capacity arises because all binary states of the hidden units can become stable memory patterns with an appropriately chosen threshold. Moreover, the distributed hidden representation, which has much lower dimensionality than the visible layer, preserves class-discriminative structure more effectively than the raw visible patterns, supporting efficient nonlinear decoding. These results establish a new regime for associative memory, enabling high-capacity, robust, and scalable architectures consistent with biological constraints.

Primary Area: applications to neuroscience & cognitive science

Submission Number: 20339