On neural networks
Jiří Beneš (1990)
Kybernetika
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Jiří Beneš (1990)
Kybernetika
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Margaris, Athanasios, Kotsialos, Efthymios, Styliadis, Athansios, Roumeliotis, Manos (2004)
Acta Universitatis Apulensis. Mathematics - Informatics
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Malyshev, V.A., Spieksma, F.M. (1997)
Mathematical Physics Electronic Journal [electronic only]
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Andrzej Kasiński, Filip Ponulak (2006)
International Journal of Applied Mathematics and Computer Science
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In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods?...
Xiangjie Kong, Yu Qi, Xiumiao Song, Guojiang Shen (2011)
Computer Science and Information Systems
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O. Ávila Åkerberg, M. J. Chacron (2010)
Mathematical Modelling of Natural Phenomena
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The interplay between intrinsic and network dynamics has been the focus of many investigations. Here we use a combination of theoretical and numerical approaches to study the effects of delayed global feedback on the information transmission properties of neural networks. Specifically, we compare networks of neurons that display intrinsic interspike interval correlations (nonrenewal) to networks that do not (renewal). We find that excitatory...
Maciej Huk (2012)
International Journal of Applied Mathematics and Computer Science
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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...
Jadranka Jović (1997)
The Yugoslav Journal of Operations Research
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D. Zhang, Q. Jiang, X. Li (2005)
Mathware and Soft Computing
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This paper describes a heuristic forecasting model based on neural networks for stock decision-making. Some heuristic strategies are presented for enhancing the learning capability of neural networks and obtaining better trading performance. The China Shanghai Composite Index is used as case study. The forecasting model can forecast the buying and selling signs according to the result of neural network prediction. Results are compared with a benchmark buy-and-hold strategy. The forecasting...