Titlebook: Neural Nets WIRN VIETRI-98; Proceedings of the 1 Maria Marinaro,Roberto Tagliaferri Conference proceedings 1999 Springer-Verlag London Limi

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Combining Neural Networks and Hidden Markov Models for Speech Recognitiong to some linguistic model. Hidden Markov models (HMM) are the most common and successful tool for ASR, allowing for high recognition performance in a variety of difficult tasks (speaker independent, large vocabulary, continuous speech). In spite of that, standard HMMs suffer from relevant limitatio

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Cellular Neural Networks: an Approach to Circuit Design and Information Processinghem particularly well-suited for VLSI implementations. To highlight this key point, the design of a low power 1.5 . CMOS CNN architecture is reported, which correctly operates within a wide range of power supply voltages. The proposed circuit is useful because it can operate at low voltages whenever

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Correlation Collapse under Persistent Input Derangement in the Nervous System. Fitting pRAM Models fmal and neuropathic animals. These correlations are learnt by a special artificial neural network, the pRAM hardware, which virtually maps on the physiological neurons’ network, on-line learning conditional probabilities from joint firing records of clusters of neurons. The most relevant probabiliti

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A New Incremental Strategy for Function Approximation by Radial Basis Function Neural Networksation functions. The novelty of the algorithm has different aspects: First, the learning procedure is accomplished modifying only the variances of the activation functions instead of the weights on the synapses; Secondly, the variances of the radial basis functions are trained by a two stage strateg

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Spatio-temporal cortical activity patterns in cognitive tasks using fMRIfor instance, in the case of voluntary actions, memory and are probably impaired in neurological disorders (e.g. epilepsy) or psychiatric disorders (e.g. schizophrenia)..The aim of the performed fMRI experiments was to evaluate multiple cortical areas involved in verbal output and input during two d

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Support Vector Machine for Nonparametric Binary Hypothesis Testingost promising methods for constructing a mathematical model only on the base of a limited amount of measured data. In this paper, we consider the application of this method to the problem of nonparametric binary hypothesis testing (bayesian setting); the main contribution of this paper is the deriva