Ultrafast all-optical biomimetic encoder for spiking neural networks

Spiking neural networks (SNNs) represent a brain-inspired computing paradigm with inherent advantages in low latency and energy efficiency, where high-fidelity conversion of analog sensory signals into optical spike trains remains a critical bottleneck for photonic neuromorphic deployment. In this work, we experimentally demonstrate an all-optical biomimetic spike encoder based on optically injected Fabry–Perot (FP) laser neuron, enabling amplitude-to-rate linear encoding with a spike rate beyond 2.0 GHz and an encoding precision of ≈5 bits. Different from previous FP laser spiking devices that only support binary spike generation, our scheme establishes a robust monotonic mapping between input optical intensity and spike firing rate, and exhibits excellent compatibility with standard 1550 nm fiber-optic communication infrastructure. We validate the encoding performance via handwritten digit recognition on the MNIST dataset using a digital spiking neural network, achieving a classification accuracy over 94.1%. Beyond static image encoding, the proposed encoder is capable of converting continuous physiological and audio signals into well-behaved spike sequences. We further construct a scalable optical neuromorphic architecture compatible with time- and wavelength-division multiplexing, supporting multi-user physiological signal access and intelligent medical diagnosis including X-ray radiography, ECG arrhythmia analysis, and heart sound classification. This work not only unveils the laser dynamical mechanism of amplitude-rate encoding in injected FP lasers, but also provides a feasible route to integrate photonic spiking encoders with existing fiber-optic networks for scalable, ultrafast, low-power neuromorphic healthcare and communication applications.