Neuromorphic encoding of image pixel data into rate-coded optical spike trains with a photonic VCSEL-neuron

Matej Hejda, Joshua Robertson, Julian Bueno, Juan Alanis, Antonio Hurtado

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
14 Downloads (Pure)


Driven by the increasing significance of artificial intelligence, the field of neuromorphic (brain-inspired) photonics is attracting increasing interest, promising new, high-speed, and energy-efficient computing hardware for key applications in information processing and computer vision. Widely available photonic devices, such as vertical-cavity surface emitting lasers (VCSELs), offer highly desirable properties for photonic implementations of neuromorphic systems, such as high-speed and low energy operation, neuron-like dynamical responses, and ease of integration into chip-scale systems. Here, we experimentally demonstrate encoding of digital image data into continuous, rate-coded, up to GHz-speed optical spike trains with a VCSEL-based photonic spiking neuron. Moreover, our solution makes use of off-the-shelf fiber-optic components with operation at telecom wavelengths, therefore making the system compatible with current optical network and data center technologies. This VCSEL-based spiking encoder paves the way toward optical spike-based data processing and ultrafast neuromorphic vision systems.

Original languageEnglish
Article number060802
Number of pages9
JournalAPL Photonics
Issue number6
Publication statusPublished - 2 Jun 2021


  • artificial intelligence
  • neuromorphic photonics
  • photonic devices
  • vertical cavity surface emitting laser


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