BACKGROUND The range of the fatty acids has been largely investigated in the plasma and erythrocytes of patients suffering from neuropsychiatric disorders. In this paper we investigate, for the first time, whether the study of the platelet fatty acids from such patients may be facilitated by means of artificial neural networks. METHODS Venous blood samples were taken from 84 patients with a D...

Large Hodgkin-Huxley (HH) neural networks were examined and the structures discussed in this article simulated a part of the rat somatosensory cortex. We used a modular architecture of the network divided into layers and sub-regions. Because of a high degree of complexity effective parallelisation of algorithms was required. The results of parallel simulations were presented. An occurrence of t...

We study the behavior of untrained neural networks whose weights and biases are randomly distributed using mean field theory. We show the existence of depth scales that naturally limit the maximum depth of signal propagation through these random networks. Our main practical result is to show that random networks may be trained precisely when information can travel through them. Thus, the depth ...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

to determine the amount of food amino acid and to spend time in the laboratories are expensive & time-consuming due to a chemical analysis. in the current laboratories, digestion nirs method is widely used for this purpose. but this method has technical limitation. therefor is important find appropriate method for estimate amount of amino acids. artificial neural network (ann) can provide a bet...

We study a simple model for a neuron function in a collective brain system. The neural network is composed of uncorrelated random scale-free network for eliminating the degree correlation of dynamical processes. The interaction of neurons is supposed to be isotropic and idealized. This neuron dynamics is similar to biological evolution in extremal dynamics with isotropic locally interaction but...

artificial neural networks (anns) are computer techniques that attempt to simulate the functionality and decision-making processes of the human brain. in the past few decades, artificial neural networks (anns) have been extensively used in a wide range of engineering applications. there are only a few applications in liquid membrane process. the objective of this research was to develop artific...

Computational intelligence approaches have gradually established themselves as a popular tool for forecasting the complicated financial markets. Forecasting accuracy is one of the most important features of forecasting models; hence, never has research directed at improving upon the effectiveness of time series models stopped. Nowadays, despite the numerous time series forecasting models propos...