Download e-book for iPad: 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.)

ISBN-10: 3642372120

ISBN-13: 9783642372124

ISBN-10: 3642372139

ISBN-13: 9783642372131

The booklet 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 geared up in topical sections on neural networks, evolutionary computation, smooth computing, bioinformatics and computational biology, complex computing, and applications.

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Read Online or Download Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings PDF

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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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MIT Press (2006) 17. : The curse of dimensionality for local kernel machines. Technical Report 1258, D´epartement d’Informatique et Recherche Op´erationnelle, Universit´e de Montr´eal (2005) 18. : The sample complexity of pattern classification with neural networks: The size of the weights is more important than the size of the network. IEEE Trans. on Information Theory 44, 525–536 (1998) 19. : Gesamelte mathematische abhandlungen, Band 1 (1950) 20. : Probabilistic Methods in Combinatorics. Academic Press (1974) 21.

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. 2 h) in the absence of synaptic homeostasis (Fig. 4). STDP was turned off during the final 800 s in order to test recall.

By constantly shifting mean activation back towards a target activity level, but maintaining the learned relative distribution of presynaptic weights, global levels of activity can be regulated [6]. g. M. Tomassini et al. ): ICANNGA 2013, LNCS 7824, pp. 20–29, 2013. c Springer-Verlag Berlin Heidelberg 2013 Scaling Balances Learning in Neocortex 21 in degenerative disorders), synaptic scaling is also capable of raising the sensitivity of neurons via AMPA receptor upregulation, so that activity levels can be restored [5].

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Adaptive and Natural Computing Algorithms: 11th International Conference, ICANNGA 2013, Lausanne, Switzerland, April 4-6, 2013. Proceedings by Toshinori Deguchi, Junya Fukuta, Naohiro Ishii (auth.), Marco Tomassini, Alberto Antonioni, Fabio Daolio, Pierre Buesser (eds.)


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