Learning and spiking dynamics in brain-like nanoscale networks

Neuromorphic approaches to computation are driven by both the low-power operation of the biological brain and ever-increasing energy consumption of modern computing systems. Percolating networks of nanoparticles are promising candidates for self-assembled neuromorphic hardware systems as they exhibit a range of brain-like properties, including neuron-like spiking dynamics and critical behaviour. Here we show that random placement of synaptic memristors within these neuron-like networks leads to changes in the spiking dynamics and to learning behaviour. We consider two models of the memristors and show that different types of memristive hysteresis lead to differing effects on the network-level spiking dynamics. We then demonstrate that mixtures of neurons and synapses exhibit potentiation and de-potentiation, i.e. learning and forgetting. These results suggest that the addition of synaptic 'memory' to self-assembled networks provides functionality that could enable new types of computation.