Optimization of hybrid photonic electrical reservoir computing

Physical Reservoir Computing is a recently developed form of Neuromorphic Computing where a non-linear physical system transfers data to higher dimensions. This information is used to train linear weights digitally to perform a specific task. Although various physical hardware have shown promising results for reservoir computing, it is not certain how to build a suitable reservoir given a set of physical components to perform a specific task. In this work, we study signal dynamics and information processing in a hybrid photonic electrical reservoir and how it can be used to build an optimal reservoir.