Dynamic Reservoir Computing with Physical Neuromorphic Networks

TL;DR

This paper explores the use of physical neuromorphic nano-electronic networks as reservoirs in reservoir computing, highlighting how network sparsity enhances nonlinear temporal processing and chaotic system prediction.

Contribution

It demonstrates the effectiveness of sparse neuromorphic networks as physical reservoirs for dynamic RC and their ability to predict chaotic time series.

Findings

Sparse networks produce more useful nonlinear outputs.

Network sparsity improves chaotic time series prediction.

Physical neuromorphic networks can learn Lorenz63 attractor behavior.

Abstract

Reservoir Computing (RC) with physical systems requires an understanding of the underlying structure and internal dynamics of the specific physical reservoir. In this study, physical nano-electronic networks with neuromorphic dynamics are investigated for their use as physical reservoirs in an RC framework. These neuromorphic networks operate as dynamic reservoirs, with node activities in general coupled to the edge dynamics through nonlinear nano-electronic circuit elements, and the reservoir outputs influenced by the underlying network connectivity structure. This study finds that networks with varying degrees of sparsity generate more useful nonlinear temporal outputs for dynamic RC compared to dense networks. Dynamic RC is also tested on an autonomous multivariate chaotic time series prediction task with networks of varying densities, which revealed the importance of network sparsity in maintaining network activity and overall dynamics, that in turn enabled the learning of the chaotic Lorenz63 system's attractor behavior.