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Neurons with internal memory have been proposed for biological andbio-inspired neural networks, adding interesting functionality. We propose andmodel a nanoscale optoelectronic neural node with charge-based time-limitedmemory and signal evaluation. Connectivity is achieved by weighted lightsignals emitted and received by the nodes. The device is based onwell-developed III-V nanowire technology, which has shown high photo-conversionefficiency, low energy consumption and sub-wavelength light concentration. Wecreate a flexible computational model of the complete artificial neural nodedevice using experimental values for wire performance. The model can simulatecombinations of nodes with different hardware derived properties and widelyvariable light interconnects. Using this model, we simulate the hardwareimplementation for two types of neural networks. First, we show thatintentional variations in the memory decay time of the nodes can significantlyimprove the performance of a reservoir network. Second, we simulate thenanowire node implementing an anatomically constrained functioning model of thecentral complex network of the insect brain and find that it functions welleven including variations in the node performance as would be found inrealistic device fabrication. Our work demonstrates the feasibility of aconcrete, variable, nanophotonic neural node with a memory. The use of variablememory time constants to open new opportunities for network performance is ageneral hardware derived feature and should be applicable for a broad range ofimplementations.

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