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Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2016; 25th International C Alessandro E.P. Villa,Paolo Masulli,Antonio Javier Confe

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Learning Multiple Timescales in Recurrent Neural Networksrtitioning hidden layers under distinct temporal constraints enables the learning of multiple timescales, which contributes to the understanding of the fundamental conditions that allow RNNs to self-organize to accurate temporal abstractions.
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,Ansatz für die eigene Untersuchung,nate dissimilar odours and generalise across similar odours, like bees do. In the most simplified connectionist description, the new input-modulation learning is shown to be asymptotically equivalent to the standard perceptron.
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Das Experiment in der Medienforschung,ic language areas, the more likely it is that region is involved in the language network. We comment on the clinical value that these structure-function connectome maps can have for planning and aiding neurosurgical procedures.
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https://doi.org/10.1007/978-3-531-90334-7ion analysis to the SPIKE-distance. The SPIKE-distance is a parameter-free measure which can quantify the distance between spike sequences. Using the SPIKE-distance, we estimate the network topology. As a result, the proposed method exhibits higher performance than the conventional method.
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https://doi.org/10.1007/978-3-322-89380-2 correct predictions for the PSSP problem and close to 79 % for the TMPTP problem, which are expected to increase with larger datasets, external rules, ensemble methods and filtering techniques. Importantly, the SCG algorithm is training the BRNN architecture approximately 3 times faster than the Backpropagation Through Time (BPTT) algorithm.
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Mapping the Language Connectome in Healthy Subjects and Brain Tumor Patientsic language areas, the more likely it is that region is involved in the language network. We comment on the clinical value that these structure-function connectome maps can have for planning and aiding neurosurgical procedures.
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