Current efforts to achieve neuromorphic computation are focused on highly organized architectures, such as integrated circuits and regular arrays of memristors, which lack the complex interconnectivity of the brain and so are unable to exhibit brain-like dynamics. New architectures are required, both to emulate the complexity of the brain and to achieve critical dynamics and consequent maximal computational performance. We show here that electrical signals from self-organized networks of nanoparticles exhibit brain-like spatiotemporal correlations and criticality when fabricated at a percolating phase transition. Specifically, the sizes and durations of avalanches of switching events are power law distributed, and the power law exponents satisfy rigorous criteria for criticality. These signals are therefore qualitatively and quantitatively similar to those measured in the cortex. Our self-organized networks provide a low-cost platform for computational approaches that rely on spatiotemporal correlations, such as reservoir computing, and are an important step toward creating neuromorphic device architectures.

We report on a real-time in situ TEM study of the coalescence of individual pairs of decahedral gold nanoparticles, which have been synthesized in solution. We observe the rate of growth of the neck that joins two particles during coalescence and compare this to classical continuum theory and to atomistic kinetic Monte Carlo simulations. We find good agreement between the observations and the simulations but not with the classical continuum model. This disagreement is attributed to the faceted nature of the particles.

We demonstrate switching behavior and quantized conductance at room temperature in percolating films of nanoparticles. Our experiments and complementary simulations show that switching and quantization result from formation of atomic scale wires in gaps between particles. These effects occur only when tunnel gaps are present in the film, close to the percolation threshold.