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 average Computing Algorithms, ICANNGA 2013, held in Lausanne, Switzerland, in April 2013.
The fifty one revised complete papers awarded have been conscientiously reviewed and chosen from a complete of ninety one submissions. The papers are geared up in topical sections on neural networks, evolutionary computation, delicate computing, bioinformatics and computational biology, complicated computing, and applications.

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

Example text

The activity rises identically in both cases when spikes occur, but the periodic sensor does not decay until the next spike event occurs, giving the step-like appearance. The values at the spike times are correct down to round-off error at the spike times. Instead of providing an arbitrary rate target for each cell, which would fundamentally affect network dynamics, the intrinsic dynamics of the network were used to provide set-points. Initially, with synaptic scaling off, activity sensors began at 0 Hz.

Bin sizes were set to 5 ms (200 Hz sampling rate). Analyses were performed using mean-subtracted MUA vectors, with spectra calculated by the multitaper spectral power estimation method, as implemented in the Python wrapper of the FORTRAN MTSpec library [16]. 1 Simulation Results Scaling Prolonged Activity during Deletion In an initial experiment, we demonstrated the usefulness of synaptic scaling by altering network dynamics through gradual removal (pruning) of cells (Fig. 2). Every 1600 s, three I or E neurons were selected at random and removed from the network by setting all their synaptic weights to zero.

Further, we propose a mathematical formalization of the observation of Bengio et al. [17, 16] that “amount of variations of a function” can cause difficulties in its representations by one-hidden-layer networks. We use the concept of variational norm with respect to a dictionary of computational units as a measure of tractability of a representation of a function by a neural network reflecting both number of hidden units and sizes of output weights. Bartlett [18] demonstrated that in some cases, the size of weights is a more important factor for successful learning than the number of network units.

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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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