Towards Optimizing Physical Reservoir Computing: A Hybrid PCPO-ESN Framework for Time Series Forecasting

In neuroscience-inspired computing, reservoir computing (RC) has emerged as a powerful model that leverages recurrent dynamics to mimic neural systems. Echo State Networks (ESNs), a prominent RC framework, are effective for modeling temporal data. However, physical RC using pulse-coupled phase oscillator (PCPO) networks, while promising for hardware systems, often struggle with flexibility, limiting their performance in time series forecasting. To address these challenges, this paper introduces a novel hybrid RC framework that combines the both states from ESNs and PCPO using two oscillators towards optimization physical RC. This hybrid-state using the oscillating events and enhance the representational capacity of the reservoir, thereby enabling the model to learn pertinent features for more accurate predictions. We evaluate our framework using the synthetic NARMA-10 dataset and the real-world Numenta Anomaly Benchmark (NAB). Experimental results show that the proposed method achieves lower mean squared error (MSE) values 7.19×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> on NARMA-10 and 33.60×10<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> on NAB, which outperforming the baseline ESN model.