Anti-periodic solutions for high-order cellular neural networks with time-varying delays.
Anti-periodic solutions for recurrent neural networks without assuming global Lipschitz conditions.
Apparition de motifs géométriques dans une membrane enzymatique
Application of Neural Networks to Modal Split Modelling
Application of Neural-Net Computing to Transient Stability Assessment and Enhancement of Electric Power Systems
Applicazioni dell’algebra differenziale all’identificabilità strutturale di modelli non lineari
Approximating the Stability Region for a Differential Equation with a Distributed Delay
We discuss how distributed delays arise in biological models and review the literature on such models. We indicate why it is important to keep the distributions in a model as general as possible. We then demonstrate, through the analysis of a particular example, what kind of information can be gained with only minimal information about the exact distribution of delays. In particular we show that a distribution independent stability region may be obtained in a similar way that delay independent...
Approximation of phenol concentration using novel hybrid computational intelligence methods
Asymptotic behaviour of some Markov operators appearing in mathematical models of biology
Asymptotical mean square stability of Cohen-Grossberg neural networks with random delay.
Automaty a mozek
Autowaves in the Model of Infiltrative Tumour Growth with Migration-Proliferation Dichotomy
A mathematical model of infiltrative tumour growth is investigated taking into account transitions between two possible states of malignant cells: proliferation and migration. These transitions are considered to depend on oxygen level in a threshold manner where high oxygen concentration allows cell proliferation, while concentration below a certain critical value induces cell migration. The infiltrative tumour spreading rate dependence on model parameters is obtained. It is shown that the tumour...
Backpropagation generalized delta rule for the selective attention Sigma-if artificial neural network
In this paper the Sigma-if artificial neural network model is considered, which is a generalization of an MLP network with sigmoidal neurons. It was found to be a potentially universal tool for automatic creation of distributed classification and selective attention systems. To overcome the high nonlinearity of the aggregation function of Sigma-if neurons, the training process of the Sigma-if network combines an error backpropagation algorithm with the self-consistency paradigm widely used in physics....
Basic terms of the theory of compartmental systems
Bifurcation analysis for a two-dimensional discrete-time Hopfield neural network with delays.
Bifurcations and biological observables
Biologically inspired robotic arm control using an artificial neural oscillator.
Boolean Biology: Introducing Boolean Networks and Finite Dynamical Systems Models to Biology and Mathematics Courses
Since the release of the Bio 2010 report in 2003, significant emphasis has been placed on initiating changes in the way undergraduate biology and mathematics courses are taught and on creating new educational materials to facilitate those changes. Quantitative approaches, including mathematical models, are now considered critical for the education of the next generation of biologists. In response, mathematics departments across the country have initiated changes to their introductory calculus sequence,...
Boundedness and stability for discrete-time delayed neural network with complex-valued linear threshold neurons.
Canonical models in mathematical neuroscience.