By Philipp Wolfrum, Christoph von der Malsburg (auth.), Joaquim Marques de Sá, Luís A. Alexandre, Włodzisław Duch, Danilo Mandic (eds.)
ISBN-10: 3540746935
ISBN-13: 9783540746935
ISBN-10: 3540746951
ISBN-13: 9783540746959
This publication is a part of a two-volume set that constitutes the refereed complaints of the seventeenth overseas convention on synthetic Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007. The 197 revised complete papers provided have been rigorously reviewed and chosen from 376 submissions. This moment quantity includes ninety nine contributions on the topic of computational neuroscience, neurocognitive stories, functions in biomedicine and bioinformatics, development reputation, facts clustering, self-organization, textual content mining and net functions, sign and instances sequence processing, imaginative and prescient and picture processing, robotics, regulate, genuine international purposes, self sufficient part research, graphs, emotion and a focus: empirical findings and neural versions, in addition to realizing and developing cognitive systems.
Read Online or Download Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II PDF
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Additional info for Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II
Example text
Moreover, two overlapping populations will have overlapping receptive fields. e. activation of spatially-close neuronal populations. e. ). From experimental observations [8] comes the main hypothesis motivating this model, that this is true not only in the primary cortex but also in the rest of the brain. Let us see in the next section how this will be practically used in the model. 3 System Workflow and Formalism Patterns and fuzzy sets. 1). The single symbol in the core of a fuzzy set characterises the set and is called its centre.
A two-layer discrete neural network and a continuous computational manifold are shown in Fig. 3. As we have seen, the measures {μi} in the computational manifolds can replace the weights {wk} in the neural network; the corresponding summation takes place inside the cell. Since the transmitter functions {τi} can extend over a large area, even the entire output manifold, many different processing elements may contribute to the concentration of neurotransmitter at any particular point (x,y,z). S. Greer functions at a given point.
In the following, we will denote with xi and yj the center of the RF of a generic neuron ij. The receptive field (say Rsij(x, y)) of neuron ij in the unisensory area s (s = a, v) is described with a gaussian function. The standard deviation of this function has been given so that the receptive fields of the visual neurons are approximately 10-15 deg in diameter, and those of acoustic neurons approximately 20-25 deg, according to data reported in literature [5]. The amplitudes of the Gaussian functions are set to 1, to establish a scale for the strength of the inputs generated by the external stimuli.
Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II by Philipp Wolfrum, Christoph von der Malsburg (auth.), Joaquim Marques de Sá, Luís A. Alexandre, Włodzisław Duch, Danilo Mandic (eds.)
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