Two recently proposed approaches to recognize temporal patterns have been proposed by Jäger with the so called Echo State Network (ESN) and by Maass with the so called Liquid State Machine (LSM). The ESN approach assumes a sort of “black-box” operability of the networks and claims a broad applicability to several different problems using the same principle. Here we propose a simplified version ...

Real-time processing of space-and-time-variant signals is imperative for perception and real-world problem-solving. In the brain, spatio-temporal stimuli are converted into spike trains by sensory neurons and projected to the neurons in subcortial and cortical layers for further processing. Reservoir Computing (RC) is a neural computation paradigm that is inspired by cortical Neural Networks (N...

We propose the Automatic-differentiated Physics-Informed Echo State Network (API-ESN). The network is constrained by physical equations through reservoir’s exact time-derivative, which computed automatic differentiation. As compared to original Network, accuracy of time-derivative increased up seven orders magnitude. This key in chaotic dynamical systems, where errors grow exponentially time. s...

A particular case of Recurrent Neural Network (RNN) was introduced at the beginning of the 2000s under the name of Echo State Networks (ESNs). The ESN model overcomes the limitations during the training of the RNNs while introducing no significant disadvantages. Although the model presents some well-identified drawbacks when the parameters are not well initialized. The performance of an ESN is ...

Prediction occurs in many biological nervous systems e.g. in the cortex [7]. We introduce a method of adapting the recurrent layer dynamics of an echo-state network (ESN) without attempting to train the weights directly. Initially a network is generated that fulfils the echo state liquid state condition. A second network is then trained to predict the next internal state of the system. In simul...

Interest in chaotic time series prediction has grown recent years due to its multiple applications fields such as climate and health. In this work, we summarize the contribution of works that use different machine learning (ML) methods predict series. It is highlighted challenge predicting larger horizon with low error, for task, majority authors datasets generated by systems Lorenz, Rössler Ma...

Transcranial electrical stimulation (tES) is a non-invasive neuromodulatory technique that alters ongoing neural dynamics by injecting an exogenous current through the scalp. Although tES protocols are becoming more common in both clinical and experimental settings, neurophysiological mechanisms which modulates cortical unknown. Most existing ignore potential effect of phasic interactions betwe...

We investigate using a previously developed spiking neuron model of layer 4 of primary visual cortex (V1) [1] as a recurrent network whose activity is consequently linearly decoded, given a set of complex visual stimuli. Our motivation is based on the following: 1) Linear decoders have proven useful in analyzing a variety of neural signals, including spikes, firing rates, local field potentials...

Abstract The El Niño-Southern Oscillation (ENSO) is a climate phenomenon that profoundly impacts weather patterns and extreme events worldwide. Here we develop method based on recurrent neural network, called echo state network (ESN), which can be trained efficiently to predict different ENSO indices despite their relatively high noise levels. To achieve this, train the ESN model low-frequency ...

Since the 2000s, social networks have grown spectacularly until they are now regarded as indispensable and introduced as a daily practice of millions of users. Enterprises have become aware of the need and the importance of these collaborative tools, and the concept of Enterprise Social Network (ESN) has now emerged. As such, OpenPaaS is an innovative ESN that aims at facilitating inter-organiz...