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In the brain, each postsynaptic neuron interconnects many presynaptic neurons and performs functions that are related to summation and recognition as well as correlation. Based on a convolution operation and nonlinear distortion function, we propose a mathematical model to explore the elementary synaptic mechanism. A four-emitter light-induced artificial synapse is implemented on an III-nitride-on-silicon platform to validate the device concept for emulating the synaptic behaviors of a biological synapse with multiple presynaptic inputs. In addition to a progressive increase in the amplitude of successive spatiotemporal excitatory postsynaptic voltages, the differences in the stimulations are remembered for signal recognition. When repetitive stimulations are simultaneously applied and last over a long period of time, resonant spatiotemporal correlation occurs because an association is formed between the presynaptic stimulations. Four resonant spatiotemporal correlations of each triple-stimulation combination are experimentally demonstrated and agree well with the simulation results. The repetitive stimulation combinations with prime number-based periods inherently exhibit the maximum capacity of resonant spatiotemporal correlation. Our work offers a new approach to building artificial synapse networks.

Spatiotemporal summation and correlation mimicked in a four-emitter light-induced artificial synapse