A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing

作者：Nahmias Mitchell A^*; Shastri Bhavin J; Tait Alexander N; Prucnal Paul R

来源：IEEE Journal of Selected Topics in Quantum Electronics, 2013, 19(5): 1800212.

DOI：10.1109/JSTQE.2013.2257700

• Cognitive computing
• excitability
• leaky integrate-and-fire (LIF) neuron
• mixed-signal
• neural networks
• neuromorphic
• optoelectronics
• photonic neuron
• semiconductor lasers
• spike processing
• ultrafast
• vertical-cavity surface-emitting lasers (VCSELs)

We propose an original design for a neuron-inspired photonic computational primitive for a large-scale, ultrafast cognitive computing platform. The laser exhibits excitability and behaves analogously to a leaky integrate-and-fire (LIF) neuron. This model is both fast and scalable, operating up to a billion times faster than a biological equivalent and is realizable in a compact, vertical-cavity surface-emitting laser (VCSEL). We show that-under a certain set of conditions-the rate equations governing a laser with an embedded saturable absorber reduces to the behavior of LIF neurons. We simulate the laser using realistic rate equations governing a VCSEL cavity, and show behavior representative of cortical spiking algorithms simulated in small circuits of excitable lasers. Pairing this technology with ultrafast, neural learning algorithms would open up a new domain of processing.

• 出版日期2013-10