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

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4 Author(s)
Nahmias, M.A. ; Dept. of Electr. Eng., Princeton Univ., Princeton, NJ, USA ; Shastri, B.J. ; Tait, A.N. ; Prucnal, P.R.

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.

Published in:

Selected Topics in Quantum Electronics, IEEE Journal of  (Volume:19 ,  Issue: 5 )

Date of Publication:

Sept.-Oct. 2013

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