Neuronal-like Irregular Spiking Dynamics in Highly Volatile Memristive Intermediate-scale AgPt-Nanoparticle Assemblies

Neuromorphic computing seeks functional materials capable of emulating brain-like dynamics to solve computational problems with time and energy efficiency, outclassing current transistor-based hardware architectures. Major efforts are focused on integrating memristive devices into highly regular circuits (i.e., crossbar arrays), where the information representation in individual memristive devices is closely oriented toward the behavior of artificial neurons. However, artificial neurons are rather rigid mathematical concepts than realistic projections of complex neuronal dynamics. Neuroscience suggests that highly efficient information representation on the level of individual neurons relies on dynamical features such as excitatory and inhibitory contributions, irregularity of firing patterns, and temporal correlations. Here, a conductive atomic force microscopy approach is applied to probe the memristive dynamics of nanoscale assemblies of AgPt-nanoparticles at the stability border of the conducting state, where physical forces causing the formation and decay of filamentary structures appear to be balanced. This unveils a dynamic regime, where the memristive response is governed by irregular firing patterns. The significance of such a dynamical regime is motivated by close similarities to excitation and inhibition-governed behavior in biological neuronal systems, which is crucial to tune biological neuronal systems into a state most suitable for information representation and computation.