Experimental study of the neuron-like dynamics of a semiconductor laser with optical feedback and current modulation

Abstract

Excitable lasers that mimic neuronal activity can be building blocks of ultra-fast neuro-inspired information processing systems, which can revolutionize the fields of optical signal processing, optical computing and artificial intelligence. To implement such photonic neurons, we must first identify low-cost and energy-efficient lasers whose excitable dynamics mimics neuronal dynamics. Here we conduct an experimental study of the dynamics of a diode laser with optical feedback from an external cavity. We focus on the response of the laser when is under weak periodic perturbations that are implemented via direct modulation of the laser pump current. We consider sinusoidal and pulsed waveforms. The dynamics of the laser intensity is compared with the dynamics of the membrane potential of a neuron, which is simulated with the FitzHugh-Nagumo (FHN) model. The analysis of the statistics of the time intervals between dropouts of the laser intensity (optical spikes) and of the time intervals between the neuron's action potentials (or spikes) unveils similarities, and also differences. The analysis of the spike rate reveals a variation with the amplitude and with the frequency of the external signal that is similar for both, the laser and the FHN neuron. Therefore, in terms of the spike rate, the laser response to a weak periodic signal mimics the response of a FHN neuron, and thus diode lasers with optical feedback can be used to implement photonic neurons. A drawback of this implementation is the length of the feedback cavity, which prevents on-chip integration.