Speaker
Description
The ability to form associations between related items is fundamental for episodic memory. In the hippocampus, single-neuron recordings in humans suggest that memories are represented by overlapping neuronal assemblies, in which 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 inevitably trigger the activation of each other, becoming functionally indistinguishable. 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. The network combines ongoing Hebbian plasticity and spontaneous activity with heterogeneous intrinsic excitability and compensatory mechanisms that stabilise the learning dynamics. We found that repeated co-activation of orthogonal assemblies led to the gradual recruitment of shared neurons, with the overlap size scaling systematically with the relative frequency 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 frequency of paired stimulations at which the overlap transitioned from partial to full overlap (i.e. merging of the assemblies). These results provide a mechanistic explanation for the formation and evolution of overlapping memory assemblies encoding associated items, bridging a gap between findings with single-neuron recordings in humans and theoretical predictions from attractor network theory.
| Preferred Presentation | Oral Presentation |
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