Monitoring of human states from streams sensor data is an appealing applicative area for Recurrent Neural Network (RNN) models. In such a scenario, Echo State (ESN) models the Reservoir Computing paradigm can represent good candidates due to efficient training algorithms, which, compared fully trainable RNNs, definitely ease embedding on edge devices.

Echo State neural networks (ESN), which are a special case of recurrent neural networks, are studied from the viewpoint of their learning ability, with a goal to achieve their greater predictive ability. In this paper we study the influence of the memory length on predictive abilities of Echo State neural networks. The conclusion is that Echo State neural networks with fixed memory length can h...

Echo state networks (ESNs) are one of two major neural network models belonging to the reservoir computing framework. Traditionally, only the weights connecting to the output neuron, termed read-out weights, are trained using a supervised learning algorithm, while the weights inside the reservoir of the ESN are randomly determined and remain unchanged during the training. In this paper, we inve...

The neural crest and neurogenic placodes are thought to be a vertebrate innovation that gives rise tomuch of the peripheral nervous system (PNS). Despite their importance for understanding chordate evolution and vertebrate origins, little is known about the evolutionary origin of these structures. Here, we investigated the mechanisms underlying the development of ascidian trunk epidermal sensor...

The neural crest and neurogenic placodes are thought to be a vertebrate innovation that gives rise tomuch of the peripheral nervous system (PNS). Despite their importance for understanding chordate evolution and vertebrate origins, little is known about the evolutionary origin of these structures. Here, we investigated the mechanisms underlying the development of ascidian trunk epidermal sensor...

The adoption of enterprise social network (ESN) for greater employee engagement and knowledge sharing practices within organisations is proliferating. However, ESN investments have thus far not resulted in expected gains in organisational benefits due to underutilisation by employees. Limited understanding of the implications of ESN use leads to a paucity of recommendations for effective use wi...

A possible alternative to topology fine-tuning for Neural Network (NN) optimization is to use Echo State Networks (ESNs), recurrent NNs built upon a large reservoir of sparsely randomly connected neurons. The promises of ESNs have been fulfilled for supervised learning tasks, but unsupervised ones, e.g. control problems, require more flexible optimization methods – such as Evolutionary Algorith...

The traditional echo state network (ESN) is a special type of a temporally deep model, the recurrent network (RNN), which carefully designs the recurrent matrix and fixes both the recurrent and input matrices in the RNN. The ESN also adopts the linear output (or readout) units to simplify the leanring of the only output matrix in the RNN. In this paper, we devise a special technique that takes ...

In this report we present a method of adding a feedback control mechanism to an echo state network (ESN) pattern generator in order to modulate its output patterns with the purpose of tracking slowly varying control targets, e.g. shift, amplitude, or frequency of an oscillatory pattern. A proofof-principle case study is presented where a basic ESN is trained to produce a stable sinewave oscilla...

The echo state network (ESN) is a cutting-edge reservoir computing technique designed to handle time-dependent data, making it highly effective for addressing time series prediction tasks. ESN inherits the more precise design of standard neural networks and relatively simple learning process has strong capacity solving nonlinear problems. It can disseminate low-dimensional information cues high...