Calibrating the Classifier: Siamese Neural Network Architecture for End-to-End Arousal Recognition from ECG Andrea Patan`e and Marta Kwiatkowska Department of Computer Science, University of Oxford

First Page		Document Content
Date: 2018-07-24 04:40:17 Artificial neural networks Computational neuroscience Applied mathematics Neuroscience Cybernetics Computational statistics Market research Deep learning Heart rate variability Emotion recognition Recurrent neural network Affective computing		Calibrating the Classifier: Siamese Neural Network Architecture for End-to-End Arousal Recognition from ECG Andrea Patan`e and Marta Kwiatkowska Department of Computer Science, University of Oxford Add to Reading List Source URL: qav.comlab.ox.ac.uk Download Document from Source Website File Size: 389,84 KB Share Document on Facebook

	Journal of Computational Neuroscience 18, 105–121, 2005 c 2005 Springer Science + Business Media, Inc. Manufactured in The Netherlands. A Cellular Mechanism for Graded Persistent Activity in a Model Neuron and Its Im DocID: 1vcJi - View Document
	munich Bernstein Center for Computational Neuroscience Munich DocID: 1vcv8 - View Document
	Blending computational and experimental neuroscience DocID: 1v9kZ - View Document
	Florentin Wörgötter Bernstein Center for Computational Neuroscience Göttingen Florentin Wörgötter studied biology and mathematics at the University of Düsseldorf, Germany. He received a Ph.D. degree, studying DocID: 1v914 - View Document
	Nonlinear synaptic interaction as a computational resource in the Neural Engineering Framework Andreas Stöckel, Aaron R. Voelker, Chris Eliasmith Centre for Theoretical Neuroscience, University of Waterloo {astoecke, ar DocID: 1uuia - View Document