A DYNAMIC ATTRACTOR MODEL OF OVERLAPPING ENGRAMS FOR ASSOCIATIVE MEMORY

The ability to form associations between related items is fundamental for episodic memory. In the human hippocampus, single-neuron recordings suggest that memories are represented by overlapping neuronal assemblies, where shared neurons encode associations between distinct but related items. Furthermore, theoretical work using static attractor networks has demonstrated that such overlaps can support associative recall within a critical range of overlap sizes, beyond which memories either remain independent or become functionally inseparable due to mutual activation. However, how such partially overlapping assemblies emerge and evolve in a dynamic network remains largely unexplored. Here, we present a computational framework that explains how partial overlaps can emerge as a function of repeated co-stimulation of initially orthogonal neuronal assemblies. Building on a previously validated dynamic attractor network model, we introduced heterogeneous baseline firing thresholds - representing variability in intrinsic excitability - to promote network stability under ongoing plasticity. We found that repeated co-activation of orthogonal assemblies led to the gradual recruitment of shared neurons, with overlap size scaling systematically with the relative fraction of paired versus individual stimulations. Moreover, by varying the distribution of intrinsic excitability across the network, we could modulate both the overlap size and the critical fraction of paired versus individual stimulations at which the system transitioned from partial overlap to full merging of the assemblies. These results provide a mechanistic account for the formation and evolution of overlapping memory engrams encoding associated items, bridging a gap between findings with single-neuron recordings in humans and theoretical predictions from attractor network theory.