Optoelectronic neuromorphic system using the neural engineering framework

Abstract

There has been a recent explosion of interest in neuromorphic computing capable of processing sophisticated and large-scale information based on spike coding, which has advantages in the implementation on electronic or optical neuromorphic systems. The neural engineering framework (NEF), a formal method for mapping attractor networks and control-theoretic algorithms to spiking neural networks, provides us a way to implement neuromorphic or numerical computing. In this paper, we implement the NEF on an optoelectronic architecture based on a photonic neuromorphic system. We discuss how electronic signals can be encoded to patterns of optical pulses, processed in the dynamic neural activity of recurrent network, and finally decoded back to the desired signals. These methods take advantage of the mechanism of spiking laser neurons and the wavelength division multiplexing protocol and explore the rapidity of optical pulses. Our work is mainly simulation with qualitative analysis. Simulation studies demonstrate three NEF principles on the proposed architecture with signals on nanosecond time scale, which run about six orders of magnitude faster than electronic counterparts on the NEF implementation. The present architecture can be also used in a broad domain of applications where more complex neuromorphic or numerical computing is necessary.