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

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

The publication constitutes the refereed lawsuits 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 offered 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, delicate computing, bioinformatics and computational biology, complex computing, and applications.

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Example text

M. [10] show that biologically plausible networks can implement marginalization near optimally for coordinate transformations, object tracking, simplified olfaction, and causal reasoning. The networks are relatively multilayer recurrent networks that implements a quadratic nonlinearity and divisive normalization. Population code is introduced to determine how neurons encode the likelihood functions and probability distributions from the Bayes approach. The marginalization of the coordinate transformation theory is proposed.

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.

Note that Hd is the set of characteristic functions of half-spaces. The set Hd is much smaller than the whole space B({0, 1}d) as it 2 has cardinality smaller than 2d [19]. The second dictionary that we consider, denoted by Sd , is closely related to Hd . It is formed by functions on {0, 1}d computable by perceptrons with the signum activation function sgn : R → {−1, 1} defined as sgn(t) := −1 for t < 0 and sign(t) := 1 for t ≥ 0. So Sd := {sgn(v · . + b) : {0, 1}d → {−1, 1} | v ∈ Rd , b ∈ R} . 3 (3) Representations of Boolean Functions by One and Two-Hidden-Layer Perceptron Networks In this section, we investigate model complexities of one and two-hidden-layer networks with Heaviside perceptrons representing Boolean functions.

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