Report on Neural-like Criticality in Ag-based Nanoparticle Networks

Abstract

Emulating the neural-like information processing dynamics of the brain provides a time and energy efficient approach for solving complex problems. While the majority of neuromorphic hardware currently developed rely on large arrays of highly organized building units, such as in rigid crossbar architectures, in biological neuron assemblies make use of dynamic transitions within highly parallel, reconfigurable connection schemes. Neuroscience suggests that efficiency of information processing in the brain rely on dynamic interactions and signal propagations which are self-tuned and non-rigid. Brain-like dynamic and avalanche criticality have already been found in a variety of self-organized networks of nanoobjects, such as nanoparticles (NP) or nanowires. Here we report on the dynamics of the electrical spiking signals from Ag-based self-organized nanoparticle networks (NPNs) at the example of monometallic Ag NPNs, bimetallic AgAu alloy NPNs and composite Ag/ZrN NPNs, which combine two distinct NP species. We present time series recordings of the resistive switching responses in each network and showcase the determination of switching events as well as the evaluation of avalanche criticality. In each case, for Ag NPN, AgAu NPN and Ag/ZrN NPN, the agreement of three independently derived estimates of the characteristic exponent provides evidence for avalanche criticality. The study shows that Ag-based NPNs offer a broad range of versatility for integration purposes into physical computing systems without destroying their critical dynamics, as the composition of these NPNs can be modified to suit specific requirements for integration.