Artificial Nanophotonic Neuron with Internal Memory for Biologically Inspired and Reservoir Network Computing

Abstract

Neurons with internal memory have been proposed for biological and bio-inspired neural networks, adding interesting functionality. We propose and model a nanoscale optoelectronic neural node with charge-based time-limited memory and signal evaluation. Connectivity is achieved by weighted light signals emitted and received by the nodes. The device is based on well-developed III-V nanowire technology, which has shown high photo-conversion efficiency, low energy consumption and sub-wavelength light concentration. We create a flexible computational model of the complete artificial neural node device using experimental values for wire performance. The model can simulate combinations of nodes with different hardware derived properties and widely variable light interconnects. Using this model, we simulate the hardware implementation for two types of neural networks. First, we show that intentional variations in the memory decay time of the nodes can significantly improve the performance of a reservoir network. Second, we simulate the nanowire node implementing an anatomically constrained functioning model of the central complex network of the insect brain and find that it functions well even including variations in the node performance as would be found in realistic device fabrication. Our work demonstrates the feasibility of a concrete, variable, nanophotonic neural node with a memory. The use of variable memory time constants to open new opportunities for network performance is a general hardware derived feature and should be applicable for a broad range of implementations.