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Recent developments in photonics include efficient nanoscale optoelectroniccomponents and novel methods for sub-wavelength light manipulation. Here, weexplore the potential offered by such devices as a substrate for neuromorphiccomputing. We propose an artificial neural network in which the weightedconnectivity between nodes is achieved by emitting and receiving overlappinglight signals inside a shared quasi 2D waveguide. This decreases the circuitfootprint by at least an order of magnitude compared to existing opticalsolutions. The reception, evaluation and emission of the optical signals areperformed by a neuron-like node constructed from known, highly efficient III-Vnanowire optoelectronics. This minimizes power consumption of the network. Todemonstrate the concept, we build a computational model based on ananatomically correct, functioning model of the central-complex navigationcircuit of the insect brain. We simulate in detail the optical and electronicparts required to reproduce the connectivity of the central part of thisnetwork, using experimentally derived parameters. The results are used as inputin the full model and we demonstrate that the functionality is preserved. Ourapproach points to a general method for drastically reducing the footprint andimproving power efficiency of optoelectronic neural networks, leveraging thesuperior speed and energy efficiency of light as a carrier of information.

Implementing an insect brain computational circuit using III-V nanowire components in a single shared waveguide optical network