By Philipp Wolfrum, Christoph von der Malsburg (auth.), Joaquim Marques de Sá, Luís A. Alexandre, Włodzisław Duch, Danilo Mandic (eds.)
This publication is a part of a two-volume set that constitutes the refereed complaints of the seventeenth foreign convention on synthetic Neural Networks, ICANN 2007, held in Porto, Portugal, in September 2007. The 197 revised complete papers offered have been conscientiously 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 popularity, information clustering, self-organization, textual content mining and web purposes, sign and occasions sequence processing, imaginative and prescient and picture processing, robotics, regulate, genuine international purposes, self reliant part research, graphs, emotion and a focus: empirical findings and neural types, in addition to figuring out and growing cognitive systems.
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Extra info for Artificial Neural Networks – ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part II
4 Discussion The present work was designed to elucidate possible neural mechanisms involved in multisensory integration in the Superior Colliculus. To this end, we developed a simple neural circuit which encompasses several mechanisms, still maintaining a moderate level of complexity. Actually, the model aspires to represent a good compromise between completeness, on one hand, and conceptual (and computational) simplicity on the other. , one area devoted to a topological organisation of visual stimuli and another area devoted to a topological organisation of auditory stimuli.
2, for an auditory (pointed line), a visual (dashed line) and a multisensory (continuous line) stimulation. These responses have been obtained by using either a single stimulus (auditory or visual), of increasing strength, or two paired stimuli (visual + auditory) located at the centre of the RF. From these curves, the dynamical range (defined according to the literature  as the difference in neuron activity at saturation and at threshold) can easily been computed. Furthermore, by way of comparison the sum of the two unisensory responses is also presented in the same figure.
1. Both the integrals with respect to the measures μi, and the summations over the transmitter functions τi, in effect perform operations analogous to the inner product with a weight vector in a single-layer neural network. Consequently, together they perform an operation analogous to a two-layer neural network. A w1,j,i Input Layer Hidden Layer ρi, μi B H w2,k,j Ni NH,G Output Layer τi G Fig. 3. A neural network (A) transforms discrete vectors, while a computational manifold (B) transforms continuous fields.