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

The publication constitutes the refereed court cases of the eleventh foreign convention on Adaptive and ordinary Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013.
The fifty one revised complete papers offered have been conscientiously 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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Extra resources for Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings

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E activity during pruning with (blue / upper) and without (green / lower) compensatory synaptic scaling. Run time 160,000 s (≈ 44 h). 5 (b) Scale factors of E cells Fig. 3. 2 Synaptic Scaling Did Not Disrupt Network Behavior The model was run for 160,000 s (≈ 44 h) to examine the effects of scaling over time on network dynamics. With scaling, activity of the E cells remained steady (Fig. 3a), and scale factors remained centered around 1 (Fig. 3b). Scaling appeared to preserve stability of the network during these extremely long runs.

By Theorem 3 and the formula (6) we get the next corollary. Corollary 2. Let d be an even integer, f : {0, 1}d → {−1, 1} a function with a Hadamard communication matrix and f (x) = m i=1 wi ϑ(ei · x + bi ) be its reprem sentation by a one-hidden-layer Heaviside perceptron network. Then i=1 |wi | = 2d/3 Ω(2 ). By Corollary 2, if a d-variable Boolean function with a Hadamard communication matrix can be represented by a one-hidden-layer Heaviside perceptron network with the number of units depending on d merely polynomially, then some of the network output weights must have exponentially large sizes.

Too many cooks? intrinsic and synaptic homeostatic mechanisms in cortical circuit refinement. Annu. Rev. Neurosci. 34, 89–103 (2011) 14. : Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex. The Journal of Neuroscience 28(7), 1709–1720 (2008) 15. : The NEURON Book. Cambridge University Press, New York (2006) 16. : A Fortran 90 library for multitaper spectrum analysis. Computers & Geosciences 35(8), 1701–1710 (2009) 17. : Clusters of hyperactive neurons near amyloid plaques in a mouse model of Alzheimer’s disease.

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