Cluster Synchronization and Associative Memory in Adaptive Networks with Neural Plasticity
Matteo Lodi, Francesco Sorrentino, Marco Storace · 2025
Adaptive networks with time-varying connectivity provide a fundamental paradigm to model networks of neurons, whose fingerprint is synaptic plasticity. We employ the stability analysis proposed in a recent paper, based on the formulation of a master stability function, to study cluster synchronization in adaptive networks with neural plasticity. We investigate how adaptation affects multistability in the network, where each stable solution encodes an archetypal pattern for auto-associative memories. This analysis is carried out with respect to the overall coupling strength, the adaptation rule, the number of nodes of the network, and the number of coexisting stable solutions. In particular, the coupling strength can be tuned to determine the maximum cluster size and the variability in the cluster sizes.