Resonate-and-fire photonic-electronic spiking neurons for fast and efficient light-enabled neuromorphic processing systems

Neuromorphic computing seeks to replicate the spiking dynamics of biological neurons for brain-inspired computation. While electronic implementations of artificial spiking neurons have dominated to date, photonic approaches are attracting increasing research interest as they promise ultrafast, energy-efficient operation with low-crosstalk and high bandwidth. Nevertheless, existing photonic neurons largely mimic integrate-and-fire models, but neuroscience shows that neurons also encode information through richer mechanisms, such as the frequency and temporal patterns of spikes. Here, we present a photonic–electronic resonate-and-fire (R&F) spiking neuron that responds to the temporal structure of high-speed optical inputs. This is based on a light-sensitive resonant tunnelling diode that produces excitable spikes in response to nanosecond, low-power (<100μW) optical signals at infrared telecom wavelengths. We experimentally demonstrate control of R&F dynamics through inter-pulse timing of the optical stimuli and applied bias voltage, achieving bandpass filtering of both analogue and digital inputs. The R&F neuron also supports optical fan-in via wavelength-division multiplexed inputs from four vertical-cavity surface-emitting lasers (VCSELs). This photonic-electronic neuron exhibits key functionalities — including spike-frequency filtering, temporal pattern recognition, and digital-to-spiking conversion — critical for neuromorphic optical processing. Our approach establishes a pathway toward low-power, high-speed temporal information processing for light-enabled neuromorphic computing. Neuromorphic computing aims to emulate the spiking dynamics of biological neurons for advanced computation, yet current photonic neurons often lack the complexity of biological systems. Here, the authors demonstrate a neuromorphic encoding scheme for pulsed signals using the resonate-and-fire neuron to transform data into a higher dimension to be used in a photonic-electronic neural network.