Reservoir Computing Using Non-Uniform Binary Cellular Automata Stefano Nichele Magnus S. Gundersen Department of Computer Science Department of Computer and Information Science Oslo and Akershus University College of Applied Sciences Norwegian University of Science and Technology Oslo, Norway Trondheim, Norway
[email protected] [email protected] Abstract—The Reservoir Computing (RC) paradigm utilizes Reservoir a dynamical system, i.e., a reservoir, and a linear classifier, i.e., a read-out layer, to process data from sequential classification tasks. In this paper the usage of Cellular Automata (CA) as a reservoir is investigated. The use of CA in RC has been showing promising results. In this paper, selected state-of-the-art experiments are Input reproduced. It is shown that some CA-rules perform better than Output others, and the reservoir performance is improved by increasing the size of the CA reservoir itself. In addition, the usage of parallel loosely coupled CA-reservoirs, where each reservoir has a different CA-rule, is investigated. The experiments performed on quasi-uniform CA reservoir provide valuable insights in CA- reservoir design. The results herein show that some rules do not Wout Y work well together, while other combinations work remarkably well. This suggests that non-uniform CA could represent a X powerful tool for novel CA reservoir implementations. Keywords—Reservoir Computing, Cellular Automata, Parallel Reservoir, Recurrent Neural Networks, Non-Uniform Cellular Au- tomata. Fig. 1. General RC framework. Input X is connected to some or all of the reservoir nodes. Output Y is usually fully connected to the reservoir nodes. I.