Computational models in neuroscience usually take the form of systems of differential
equations. The behaviour of such systems is the subject of dynamical systems theory …

Reservoir computing is a machine learning method that is closely linked to dynamical
systems theory. This connection is highlighted in a brief introduction to the general concept …

The van der Pol oscillator has historical and practical significance to spiking neural
networks. It was proposed as one of the first models for heart oscillations, and it has been …

Physics-based simulations are important for elucidating the fundamental mechanisms
behind the time-varying complex ionospheric conditions, such as ionospheric potential …

Traditional neural network models of associative memories were used to store and retrieve
static patterns. We develop reservoir-computing based memories for complex dynamical …

Reservoir computing is a machine learning paradigm that uses a structure called a reservoir,
which has nonlinearities and short-term memory. In recent years, reservoir computing has …

This study uses a machine learning technique based on the Reservoir Computing (RC)
model to predict the surge, sway, heave, roll, pitch, and yaw (6-DOF) motions of the KVLCC2 …

In response to the growing need for efficient processing of temporal information,
neuromorphic computing systems are placing increased emphasis on the switching …

The echo state network (ESN) is an excellent machine learning model for processing time-
series data. This model, utilising the response of a recurrent neural network, called a …

Long-term time series forecasting is widely used in various real-world applications, such as
weather, traffic, energy, healthcare, etc. Recently, time series decomposition techniques …