Programmable Functional Connectivity and Synchronous Activity in Resistive Switching Self-Assembled Nanostructured Networks

The efficiency of biological data processing systems is based on their adaptive connectivity and on the mutual interactions of their elements at different scales. These features are not substantially present in the design of electronic architectures based on conventional integrated circuits. The exploitation of self-assembled systems characterized by nonlinear dynamics is actively investigated as a viable alternative strategy to develop energy-efficient data processing devices. However, the encoding of external stimuli and the decoding of information from the analog response of such systems is still a challenge. Here we characterize the functional connectivity and the synchronicity between active sites in cluster-assembled nanostructured Au films showing resistive switching behavior by a combined approach based on micro-thermography and electrical measurements. We investigate the complex mechanisms involved in the network reorganization leading to its resistive switching activity and we identify the interplay between network dimensions and its emerging electrical behavior. We investigate the control on the synchronous activity and on the connectivity of the micrometric active sites which rule the emerging network dynamics and determe the performance of data processing devices. This activity is described using data analysis techniques commonly used in neuroscience, which are proposed for the first time to characterize neuromorphic systems.