摘要
Reservoir computing has been an intriguing paradigm in the field of artificial intelligence and machine learning that draws inspiration from the complex dynamics of recurrent neural networks found in biological systems. Unlike traditional neural networks, reservoir computing separates the training of a fixed, randomly connected ‘reservoir’layer from a simpler ‘readout’ layer. This distinctive architecture allows the reservoir to process information in a highly dynamic and nonlinear manner.
