We review attempts that have been made towards understanding the computational properties and mechanisms of input-driven dynamical systems like RNNs, and reservoir computing networks in particular. We provide details on methods that have been developed to give quantitative answers to the questions above. Following this, we show how self-organization may be used to improve reservoirs for better ...

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 ...

â  abstract: in this paper, artificial neural network (ann) was used for modeling the nonlinear structure of a debutanizer column in a refinery gas process plant. the actual input-output data of the system were measured in order to be used for system identification based on root mean square error (rmse) minimization approach. it was shown that the designed recurrent neural network is able to pr...

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power sp...

| We investigate the learning of de-terministic nite-state automata (DFA's) with recurrent networks with a single input neu-ron, where each input symbol is represented as a temporal pattern and strings as sequences of temporal patterns. We empirically demonstrate that obvious temporal encodings can make learning very diicult or even impossible. Based on preliminary results, we formulate some hy...

We derive analytical expressions of local codimension-1 bifurcations for a fully connected, additive, discrete-time recurrent neural network (RNN), where we regard the external inputs as bifurcation parameters. The complexity of the bifurcation diagrams obtained increases exponentially with the number of neurons. We show that a three-neuron cascaded network can serve as a universal oscillator, ...

The principle of maximizing mutual information is applied to learning overcomplete and recurrent representations. The underlying model consists of a network of input units driving a larger number of output units with recurrent interactions. In the limit of zero noise, the network is deterministic and the mutual information can be related to the entropy of the output units. Maximizing this entro...

A hybrid genetic algorithm is proposed for training neural networks with recurrent connections. A fully connected recurrent ANN model is employed and tested over a number of various problems. Simulation results are presented for three problems: generation of a stable limit cycle, sequence recognition and storage and reproduction of temporal sequences. 1.Introduction Although the recurrent ANN m...

OBJECTIVE To construct and optimize a neural network that is capable of predicting the occurrence of recurrent aphthous ulceration (RAU) based on a set of appropriate input data. PARTICIPANTS AND METHODS Artificial neural networks (ANN) software employing genetic algorithms to optimize the architecture neural networks was used. Input and output data of 86 participants (predisposing factors an...