  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.
