Neuromodulation-inspired gated associative memory networks: extended memory retrieval and emergent multistability

Classical autoassociative memory models have been central to understanding emergent computations in recurrent neural circuits across diverse biological contexts. However, they typically neglect neuromodulatory agents that are known to strongly shape memory capacity and stability. Here we introduce a minimal, biophysically motivated associative memory network where neuropeptide-like signals are modeled by a self-adaptive, activity-dependent gating mechanism. Using many-body simulations and dynamical mean-field theory, we show that such gating fundamentally reorganizes the attractor structure: the network bypasses the classical spin-glass transition, maintaining robust, high-overlap retrieval far beyond the standard critical capacity, without shrinking basins of attraction. Mechanistically, the gate stabilizes transient "ghost" remnants of stored patterns even far above the Hopfield limit, converting them into multistable attractors. These results demonstrate that neuromodulation-like gating alone can dramatically enhance associative memory capacity, eliminate the sharp Hopfield-style catastrophic breakdown, and reshape the memory landscape, providing a simple, general route to richer memory dynamics and computational capabilities in neuromodulated circuits and neuromorphic architectures.