Adaptive and Natural Computing Algorithms: 11th by Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.),

By Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)

The booklet constitutes the refereed court cases of the eleventh overseas convention on Adaptive and common Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013.
The fifty one revised complete papers provided have been rigorously reviewed and chosen from a complete of ninety one submissions. The papers are equipped in topical sections on neural networks, evolutionary computation, gentle computing, bioinformatics and computational biology, complex computing, and applications.

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Additional resources for Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings

Example text

Then, by the linear coordinate transformation, the integrated variance is shown as 2 σ C2 = σ A2 + σ B2 . This shows that the integrated gain of the neural activity becomes 1/ g C = 1/ g A + 1/ g B , which may be written as gC = g Ag B gA + gB (15) Thus, the gains transform via a quadratic nonlinearity with divisive normalization. The response activity, which is proportional to the gain in equation (15), is replaced as [10] w r k A B ij i j rkC = r ij  (α r + βlB rl B ) A A l l (16) l where the w ' s , α ' s and β ' s are coefficient weights.

2 blue / upper line), with brief activity peaks caused when the inherent delay in the activity sensor led to over-compensation. These activity peaks do not correspond to deletion times, but rather to emergent instabilities in the resulting damaged network. Indeed, the network remains stable for nearly half a day following the onset of deletion after 800 s. The over-compensation can be adjusted to some degree, although not eliminated, by altering the scaling parameters β and γ (not shown). 24 M.

ICANNGA 2013, LNCS 7824, pp. 30–39, 2013. c Springer-Verlag Berlin Heidelberg 2013 Can Two Hidden Layers Make a Difference? 31 from nonlinear approximation theory. , [10] and references therein). Inspection of such upper bounds led to descriptions of families of functions that can be well approximated by one-hidden-layer networks with reasonably small numbers of computational units of various types. On the other hand, limitations of computational capabilities of one-hidden-layer networks are less understood.

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