Emergence of overlapping assemblies in a dynamic attractor model of associative memory

Abstract Forming a memory involves linking the different aspects that form an experience. Single-neuron recordings in humans suggest that, in the hippocampus, memories are encoded by partially overlapping engrams, with partial overlaps representing associations. Furthermore, theoretical work with static attractor networks has demonstrated that partial overlaps between engrams can support associative recall within a limited range, beyond which memories remain independent or merge into one. However, how overlaps emerge through plasticity in an attractor network remains unknown. Here we developed a modelling approach in order to explain how partial overlaps encoding associations can emerge as a function of repeated co-stimulation. We built on a previously validated dynamic attractor network model to which we introduced heterogeneous baseline firing rates as an additional stabilizing mechanism. We found that repeated co-stimulation of initially orthogonal engrams could induce the formation of shared representations, with the overlap size scaling with the fraction of paired to individual stimulations and modulated by neuronal excitability. These findings provide a mechanistic link between experimental observations of overlapping engrams and theoretical predictions about their functional role, offering new insights into the hippocampal coding of associative memory in humans, also informing the design of flexible neuromorphic memory systems.