A leaky integrate-and-fire laser neuron for ultrafast cognitive computing

Mitchell A. Nahmias, Bhavin J. Shastri, Alexander N. Tait, Paul R. Prucnal

Research output: Contribution to journalArticlepeer-review

228 Scopus citations


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.

Original languageEnglish (US)
Article number6497478
JournalIEEE Journal on Selected Topics in Quantum Electronics
Issue number5
StatePublished - 2013

All Science Journal Classification (ASJC) codes

  • Atomic and Molecular Physics, and Optics
  • Electrical and Electronic Engineering


  • 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)


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