Abstract

Spiking neural networks (SNNs) are inspired by biological neural networks, where transfer of information takes place via momentary pulses, called action potential or spikes. Unlike traditional neurons in Artificial neural networks (ANNs), spike-events are sparse and capable of encoding temporal information. Considering this cutting edge benefits provided by this, for e.g. hardware friendliness and sparsity induced energy efficiency [1], we have designed and experimentally implemented a photonic liquid state machine (LSM) [2] based on photonic recurrent SNNs [3]. Our scalable proof-of-concept experiment comprises more than 30,000 excitable neurons and is the first large-scale test-bed system for next-generation bio inspired learning concepts in photonic ANNs.

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