Neuromorphic photonics is a new paradigm for ultra-fast neuro-inspired optical computing that canrevolutionize information processing and artificial intelligence systems. To implement practicalphotonic neural networks is crucial to identify low-cost energy-efficient laser systems that can mimicneuronal activity. Here we study experimentally the spiking dynamics of a semiconductor laser withoptical feedback under periodic modulation of the pump current, and compare with the dynamics of aneuron that is simulated with the stochastic FitzHugh–Nagumo model, with an applied periodicsignal whose waveform is the same as that used to modulate the laser current. Sinusoidal and pulse-down waveforms are tested. Wefind that the laser response and the neuronal response to the periodicforcing, quantified in terms of the variation of the spike rate with the amplitude and with the frequencyof the forcing signal, is qualitatively similar. We also compare the laser and neuron dynamics usingsymbolic time series analysis. The characterization of the statistical properties of the relative timing ofthe spikes in terms of ordinal patterns unveils similarities, and also some differences. Our resultsindicate that semiconductor lasers with optical feedback can be used as low-cost, energy-efficientphotonic neurons, the building blocks of all-optical signal processing systems; however, the length ofthe external cavity prevents optical feedback on the chip.

Peer Reviewed