models neural networks di domany hemmen (93 risultati)

Condizione: Sehr gut. Zustand: Sehr gut | Seiten: 372 | Sprache: Englisch | Produktart: Bücher.

24 x 16. Corrected second printing. 347 Seiten. Mit 78 figures. OPb. Ordnungsgemäß aus einer Universitäts-Bibliothek ausgesondert (Stempel, Rückenschild). Gut erhalten. Sprache: Deutsch Gewicht in Gramm: 800.

Softcover reprint of the original 1st ed. 2002. 15 x 23 cm. 426 pages. Paperback. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.

Condizione: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.

Condizione: New. pp. xiii + 413.

Condizione: Très bon. PHYSICS OF NEURAL NETWORKS - MODELS OF NEURAL NETWORKS I - SECOND UPDATED EDITION | Domany, van Hemmen, Schulten | Springer, 1995. In-8° cartonné, 355 pages. Couverture propre . Dos solide. Intérieur frais sans soulignage ou annotation. Exemplaire de bibliothèque : petit code barre en pied de 1re de couv., cotation au dos, rares et discrets petits tampons à l'intérieur de l'ouvrage. Très bon état général pour cet ouvrage. [Ba 56+].

Condizione: Gut. Zustand: Gut | Seiten: 311 | Sprache: Englisch | Produktart: Bücher.

Hardcover. 345pp. Some neat highlighting on about 10 pages, else VG condition.

Taschenbuch. Condizione: Neu. Neuware -One of the most challenging and fascinating problems of the theory of neural nets is that of asymptotic behavior, of how a system behaves as time proceeds. This is of particular relevance to many practical applications. Here we focus on association, generalization, and representation. We turn to the last topic first. The introductory chapter, 'Global Analysis of Recurrent Neural Net works,' by Andreas Herz presents an in-depth analysis of how to construct a Lyapunov function for various types of dynamics and neural coding. It includes a review of the recent work with John Hopfield on integrate-and fire neurons with local interactions. The chapter, 'Receptive Fields and Maps in the Visual Cortex: Models of Ocular Dominance and Orientation Columns' by Ken Miller, explains how the primary visual cortex may asymptotically gain its specific structure through a self-organization process based on Hebbian learning. His argu ment since has been shown to be rather susceptible to generalization.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 328 pp. Englisch.

Taschenbuch. Condizione: Neu. Neuware -Since the appearance of Vol. 1 of Models of Neural Networks in 1991, the theory of neural nets has focused on two paradigms: information coding through coherent firing of the neurons and functional feedback. Information coding through coherent neuronal firing exploits time as a cardinal degree of freedom. This capacity of a neural network rests on the fact that the neuronal action potential is a short, say 1 ms, spike, localized in space and time. Spatial as well as temporal correlations of activity may represent different states of a network. In particular, temporal correlations of activity may express that neurons process the same 'object' of, for example, a visual scene by spiking at the very same time. The traditional description of a neural network through a firing rate, the famous S-shaped curve, presupposes a wide time window of, say, at least 100 ms. It thus fails to exploit the capacity to 'bind' sets of coherently firing neurons for the purpose of both scene segmentation and figure-ground segregation. Feedback is a dominant feature of the structural organization of the brain. Recurrent neural networks have been studied extensively in the physical literature, starting with the ground breaking work of John Hop field (1982).Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 364 pp. Englisch.