Network and Single-Node Criticality in Cluster-Assembled Nanostructured Systems: Implications for Adaptive Neuromorphic Devices

Abstract

Self-organized criticality (SOC) has emerged as a hallmark of neuronal biological systems, arising from their highly interconnected architecture and enabling efficient processing, transmission, and adaptation to analog signals. Inspired by these observations, artificial systems for neuromorphic computing that exhibit critical dynamics have been extensively investigated. Among them, self-assembled materials have shown considerable promise, displaying spontaneous collective behavior and scale-invariant dynamics. However, the ability to controllably tune criticality in such systems remains largely unexplored, particularly through the reversible programming of their functional connectivity. Here, we demonstrate that cluster-assembled materials can dynamically self-reorganize their functional network connectivity in response to different electrical stimuli. By exploiting this adaptive reconfiguration, the system can be reversibly driven toward distinct dynamical regimes, including both network-level and single-node near-critical states. These results establish cluster-assembled materials as a versatile hardware platform for the investigation and control of critical phenomena, while opening opportunities for adaptive neuromorphic computing and bio-inspired information processing.