Online spike-based recognition of digits with ultrafast microlaser neurons

Classification and recognition tasks performed on photonic hardware-based neural networks often require at least one offline computational step, such as in the increasingly popular reservoir computing paradigm. Removing this offline step can significantly improve the response time and energy efficiency of such systems. We present numerical simulations of different algorithms that utilize ultrafast photonic spiking neurons as receptive fields to allow for image recognition without an offline computing step. In particular, we discuss the merits of event, spike-time and rank-order based algorithms adapted to this system. These techniques have the potential to significantly improve the efficiency and effectiveness of optical classification systems, minimizing the number of spiking nodes required for a given task and leveraging the parallelism offered by photonic hardware.

Automated summary

Classification and recognition tasks on photonic hardware-based neural networks often require an offline computational step, but removing this step can improve response time and energy efficiency. We present numerical simulations of different algorithms using ultrafast photonic spiking neurons as receptive fields for image recognition without offline computing. We discuss the merits of event, spike-time, and rank-order based algorithms adapted to this system, which can significantly improve the efficiency and effectiveness of optical classification systems.