Recent developments in photonics include efficient nanoscale optoelectronic
components and novel methods for sub-wavelength light manipulation. Here, we
explore the potential offered by such devices as a substrate for neuromorphic
computing. We propose an artificial neural network in which the weighted
connectivity between nodes is achieved by emitting and receiving overlapping
light signals inside a shared quasi 2D waveguide. This decreases the circuit
footprint by at least an order of magnitude compared to existing optical
solutions. The reception, evaluation and emission of the optical signals are
performed by a neuron-like node constructed from known, highly efficient III-V
nanowire optoelectronics. This minimizes power consumption of the network. To
demonstrate the concept, we build a computational model based on an
anatomically correct, functioning model of the central-complex navigation
circuit of the insect brain. We simulate in detail the optical and electronic
parts required to reproduce the connectivity of the central part of this
network, using experimentally derived parameters. The results are used as input
in the full model and we demonstrate that the functionality is preserved. Our
approach points to a general method for drastically reducing the footprint and
improving power efficiency of optoelectronic neural networks, leveraging the
superior speed and energy efficiency of light as a carrier of information.Comment: 28 pages, 6 figures; supplementary information 15 pages, 8 figure
