Reservoir computing is a computational framework suited for temporal/sequential data
processing. It is derived from several recurrent neural network models, including echo state …

Memristive systems and devices are potentially available for implementing reservoir
computing (RC) systems applied to pattern recognition. However, the computational ability …

Machine learning methods are ubiquitous in communication systems and have proven
powerful for applications including radio-frequency (RF) fingerprinting, automatic modulation …

Reservoir computing is one of the computational frameworks based on recurrent neural
networks for learning sequential data. We study the memristive reservoir computing where a …

First we discuss the essence of neural networks, which are the bases of modern artificial
intelligence (AI), to examine the relationship between the neural fundamental framework …

We propose a method of dimension reduction of high dimensional time series data by
reservoir computing. The proposed method is a generalization of random projection …

In this paper, we discuss the significance of complex-valued neural-network (CVNN)
framework in energy-efficient neural networks, in particular in wave-based reservoir …

This chapter is devoted to the semiconductor laser with a saturable absorber as a device
that can operate as an excitable medium. After a short historical introduction, the …

This paper investigates the spatial distribution of information effective for function learning in
a spin-wave reservoir-computing garnet chip. We map the neural weights of a readout …

This study introduces a novel reservoir computing framework featuring an evolvable
topology, optimized for minimal clustering degree and path length, which are key …