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

Coupled oscillators are highly complex dynamical systems, and it is an intriguing concept to
use this oscillator dynamics for computation. The idea is not new, but is currently the subject …

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural
network in which neurons are randomly connected. Once initialized, the connection …

This study presents a neural network which uses filters based on logistic map**
(LogNNet). LogNNet has a feedforward network structure, but possesses the properties of …

Transistor-based chaotic oscillators are known to realize highly diverse dynamics despite
having elementary circuit topologies. This work investigates, numerically and experimentally …

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-
optic modulator and field programmable gate array (FPGA). It implements delay lines and …

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 …

We present a framework of wave-based neuromorphic computing aiming at brain-like
capabilities and efficiencies with nanoscale device integration. We take advantage of the …

In this letter, we propose a compact frequency programmable oscillator with an NbO 2-
based insulator-metal transition (IMT) device and three-terminal Li-based electro-chemical …

We propose a concept for reservoir computing on oscillators using the high-order
synchronization effect. The reservoir output is presented in the form of oscillator …