Titlebook: Artificial Neural Networks and Machine Learning- ICANN 2011; 21st International C Timo Honkela,W?odzis?aw Duch,Samuel Kaski Conference proc

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Cornelius Bickel,Sebastian Klaukeral output signals are encoded as a sum of shifted power-law kernels. Simple greedy thresholding can compute this encoding, and spike-trains are then exactly the signal’s fractional derivative. Fractionally predictive spike-coding exploits natural statistics and is consistent with observed spike-rat

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debouch

https://doi.org/10.1007/978-3-322-85606-7er to interact in this complex and changing environment, according to the active perception theory, the agent needs to learn the correlations between its actions and the changes they induce in the environment. In the perspective of a bio-inspired architecture for the learning of multi-modal correlat

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https://doi.org/10.1007/978-3-322-85606-7with the forward projection. However, the wide terminal arbors of individual axons limit the precision of such anatomical reciprocity. This leaves open the question of whether more precise reciprocal connectivity is obtainable through the adjustment of synaptic strengths. We have found that such a s

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hypertension

https://doi.org/10.1007/978-3-662-33998-5ple classifiers were introduced. An interesting kind of methods creates a hierarchy of sub-problems by clustering prototypes of each one of the classes, but the solution produced by the clustering stage is heavily influenced by the label’s information. In this work we introduce a new strategy to sol

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Fermat’s Last Theorem for Amateursatching substructures are considered independently from their position within the trees. However, when a match happens in similar positions, more strength could reasonably be given to it. Here, we give a systematic way to enrich a large class of tree kernels with this kind of information without aff