Open Access
IEEE Journal of Selected Topics in Quantum Electronics, volume 19, issue 5, pages 1-12
A Leaky Integrate-and-Fire Laser Neuron for Ultrafast Cognitive Computing
Mitchell A Nahmias
1
,
Bhavin J Shastri
1
,
Alexander N Tait
1
,
Paul R. Prucnal
1
Publication type: Journal Article
Publication date: 2013-09-01
scimago Q1
wos Q1
SJR: 1.012
CiteScore: 9.9
Impact factor: 5.1
ISSN: 1077260X, 15584542
Atomic and Molecular Physics, and Optics
Electrical and Electronic Engineering
Abstract
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.
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Metrics
271
Total citations:
271
Citations from 2024:
57
(21%)