arXiv2025
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 in which 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. Mechanistically, when the gates act as fully open or fully closed switches, gating stabilizes transient "ghost" remnants of stored patterns even far above the Hopfield limit, converting a single memory fixed-point attractor into a cluster of fixed points. The retrieved state is set by the initial overlap rather than quantized to the stored pattern. This graded dependence may be exploited for input-driven computation with persistent activity. These results establish that neuromodulation-like gating alone, without modifying synaptic weights or introducing explicit higher-order couplings, can dramatically expand memory capacity, eliminate catastrophic breakdown, and reshape a discrete attractor landscape into a rich continuum, suggesting a general and biologically accessible route to enhanced computational capabilities in neural circuits and neuromorphic architectures.