Journal
IEEE JOURNAL OF SELECTED TOPICS IN QUANTUM ELECTRONICS
Volume 19, Issue 5, Pages -Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTQE.2013.2257700
Keywords
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)
Categories
Funding
- Lockheed Martin Advanced Technology Laboratory through the IRAD program
- Lockheed Martin Corporation through the Corporate University Research Program
- NSF MIRTHE Center at Princeton University
- Pyne Fund
- Essig Enright Fund for Engineering in Neuroscience
- National Science Foundation Graduate Research Fellowship (NSF-GRF)
- National Sciences and Engineering Research Council of Canada (NSERC)
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available