Dynamic Reservoir Computing with Physical Neuromorphic Networks
This work addresses the challenge of optimizing physical reservoir structures for neuromorphic computing, though it appears incremental as it builds on existing RC methods with specific network modifications.
The study tackled the problem of using physical neuromorphic networks as dynamic reservoirs in Reservoir Computing, finding that sparser networks produce more useful nonlinear temporal outputs and enable learning of chaotic attractor behavior in a Lorenz63 system prediction task.
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.