Modularity-dependent storage of dynamic spiking patterns: Bridging micro- and mesoscopic representations

Year: 2026

Authors: Angiolelli M., De Candia A., Sorrentino P., Filippi S., Chiodo L., Cherubini C., Scarpetta S.

Autors Affiliation: Univ Campus Biomed Roma, Dept Engn, Via Alvaro Portillo 21, I-00128 Rome, Italy; Univ Naples Federico II, Dept Phys, Naples, Italy; INFN, Sez Napoli, Naples, Italy; Aix Marseille Univ, Inst Neurosci Syst, INSERM, INS, 27 Bd Jean Moulin, F-13005 Marseille, France; Univ Naples Parthenope, Dept Motor Sci & Wellness, Naples, Italy; Italian Natl Res Council CNR INO, Ist Nazl Ottica, Largo Enrico Fermi 6, I-50125 Florence, Italy; Univ Campus Biomed Roma, Dept Sci & Biotechnol, Via Alvaro Portillo 21, I-00128 Rome, Italy; ICRANet Int Ctr Relativist Astrophys Network, Piazza Repubblica 10, I-65122 Pescara, Italy; Univ Salerno, Dept Phys ER Caianiello, Via Giovanni Paolo II 132, Fisciano, Italy; INFN, Sez Napoli, Gr Coll Salerno, Via Giovanni Paolo II, Fisciano, Italy.

Abstract: Biological systems rely on asynchronous and temporally overlapping dynamics, allowing for the concurrent activation of multiple processes. This principle is particularly evident in brain function, where cognitive tasks engage distributed, interacting regions rather than sequentially isolated ones. To investigate the mechanisms enabling such coordination, we study a modular spiking neural network composed of leaky integrate-and-fire neurons and governed by spike-timing-dependent plasticity. Our model stores modular spatiotemporal patterns both at the mesoscopic level (sequences of modules) and at the microscopic level (precise spike timings) and includes a parameter, eta, which regulates the degree of temporal overlap between modules’ activations. By tuning eta, the network transitions from sequential to overlapping regimes, ranging from synfire chainlike dynamics to fully co-activated modules. We investigate how the temporal structure influences the network’s capacity to encode and selectively retrieve multiple dynamical patterns while considering biological constraints such as the cost of long-range connectivity. Our results offer insight into how spatiotemporal coding and network organization support robust, large-scale memory storage and replay.

Journal/Review: PHYSICAL REVIEW E

Volume: 113 (5)      Pages from: 54302-1  to: 54302-16

More Information: M.A., S.F., C.C., and L.C. acknowledge the Italian National Group for Mathematical Physics, GNFM-INdAM. S.F. acknowledges ICRANet. Thi s research has received funding from ’European Union – NextGenerationEU – PNRR’, MUR code IR0000011, CUP B51E22000150006, project ’EBRAINS-Italy – European Brain ReseArch INfrastructureS Italy’.
KeyWords: Neuronal Avalanches; Hippocampal Replay; Memory; Plasticity; Sequences; Networks; Model; Connectivity; Organization; Recall
DOI: 10.1103/rklz-gkqn