Self-organized nanoscale networks: are neuromorphic properties conserved in realistic device geometries?

Abstract

Abstract Self-organised nanoscale networks are currently under investigation because of their potential to be used as novel neuromorphic computing systems. In these systems, electrical input and output signals will necessarily couple to the recurrent electrical signals within the network that provide brain-like functionality. This raises important questions as to whether practical electrode configurations and network geometries might influence the brain-like dynamics. We use the concept of criticality (which is itself a key charactistic of brain-like processing) to quantify the neuromorphic potential of the devices, and find that in most cases criticality, and therefore optimal information processing capability, is maintained. In particular we find that devices with multiple electrodes remain critical despite the concentration of current near the electrodes. We find that broad network activity is maintained because current still flows through the entire network. We also develop a formalism to allow a detailed analysis of the number of dominant paths through the network. For rectangular systems we show that the number of pathways decreases as the system size increases, which consequently causes a reduction in network activity.