The present paper proposes a recurrent neural network model and learning algorithm that can acquire the ability to generate desired multiple sequences. The network model is a dynamical system in which the transition function is a contraction mapping, and the learning algorithm is based on the gradient descent method. We show a numerical simulation in which a recurrent neural network obtains a m...

Small-world and scale-free networks are known to be more easily synchronized than regular lattices, which is usually attributed to the smaller network distance between oscillators. Surprisingly, we find that networks with a homogeneous distribution of connectivity are more synchronizable than heterogeneous ones, even though the average network distance is larger. We present numerical computatio...

In this paper, we investigate the use of adaptive extended Luenberger state estimators for general nonlinear and possibly time-varying systems. We identify the connection between the extended Luenberger observer and Grossberg’s additive model for dynamic neural networks. The association between dynamic neural networks and the Luenberger observer leads to an obvious modification on the proposed ...

This paper presents an adaptive output feedback stabilisation method based on neural networks for nonlinear non-minimum phase systems. The proposed controller comprises a linear, a neuro-adaptive, and an adaptive robustifying parts. The neural network is designed to approximate the matched uncertainties of the system. The inputs of the neural network are the tapped delays of the system inputout...

A model of auditory grouping is described in which auditory attention plays a key role. The model is based upon an oscillatory correlation framework, in which neural oscillators representing a single perceptual stream are synchronised, and are desynchronised from oscillators representing other streams. The model suggests a mechanism by which attention can be directed to the high or low tones in...

Little is known theoretically about the associative memory capabilities of neural networks in which information is encoded not only in the mean firing rate but also in the timing of firings. Particularly, in the case that the fraction of active neurons involved in memorizing patterns becomes small, it is biologically important to consider the timings of firings and to study how such considerati...

In order to recognise speech in a background of other sounds, human listeners must solve two perceptual problems. First, the mixture of sounds reaching the ears must be parsed to recover a description of each acoustic source, a process termed ‘auditory scene analysis’. Second, recognition of speech must be robust even when the acoustic evidence is missing due to masking by other sounds. This pa...

We study a simple extended model of oscillator neural networks capable of storing sparsely coded phase patterns, in which information is encoded both in the mean firing rate and in the timing of spikes. Applying the methods of statistical neurodynamics to our model, we theoretically investigate the model’s associative memory capability by evaluating its maximum storage capacities and deriving i...

This thesis is concerned with the function and implementation of synchronization in networks of oscillators. Evidence for the existence of synchronization in cortex is reviewed and a suitable architecture for exhibiting synchronization is defined. A number of factors which affect the performance of synchronization in networks of laterally coupled oscillators are investigated. It is shown that a...

In this paper we propose a new method to estimate parameters of a dynamical system from observation data on the basis of a neural network collocation method. We construct an object function consisting of squared residuals of dynamical model equations at collocation points and squared deviations of the observations from their corresponding computed values. The neural network is then trained by o...