Complexity of brain-like signals in self-organised nanoscale networks

The biological brain is comprised of a complex, interconnected, self-assembled network of neurons and synapses. This network enables efficient and accurate information processing, unsurpassed by any other known computational system. Percolating networks of nanoparticles (PNNs) are complex, interconnected, self-assembled systems that exhibit many emergent brain-like characteristics. Notably, neuron-like spiking patterns from PNNs have been shown to be critical, similar to signals from the cortex. PNNs are therefore an appealing candidate for neuromorphic computational systems. Here, the inherent complexity of the patterns of switching events generated by PNNs is explored using several different measures. We begin by defining qualitative measures of spatial, temporal, and spatio-temporal complexity, and then investigate a quantitative measure of complexity that was developed for analysis of patterns of spikes from neurons in the cortex. We discuss adaptations of the method that are required for data from the electronic devices of interest and the impact of various pre-processing procedures on the analysis. Through these measures, it is shown that the neuron-like spiking patterns from PNNs are indeed complex and are clearly distinct from random and ordered data.