Extension of Recurrent Kernels to different Reservoir Computing topologies

As kernel methods require the calculation of scalar products between all pairs of input points, recurrent Reservoir Computing is a machine learning technique used kernels offer an interesting alternative to Reservoir Computing for training Recurrent Neural Networks, which fixes the internal when the number of data points is limited. Additionally, recurrent weights of the network and trains only a linear layer, resulting kernels have been useful for theoretical studies, such in faster training times [9]. Its simplicity and effectiveness have as stability analysis in Reservoir Computing, as they provide a made it a popular choice for various tasks [12]. Additionally, deterministic limit with analytical expressions [2]. the random connections within Reservoir Computing networks Prior research on Recurrent Kernels has been mainly limited make them a useful framework for comparison with biological to vanilla Reservoir Computing and structured transforms.