Neural Variational Inference and Learning in Belief Networks

First Page		Document Content
Date: 2014-06-04 22:03:38 Neural networks Computational neuroscience Graphical models Variational Bayesian methods Bayesian network Boltzmann machine Machine learning Bayesian inference Expectation–maximization algorithm Statistics Bayesian statistics Networks		Neural Variational Inference and Learning in Belief Networks Add to Reading List Source URL: arxiv.org Download Document from Source Website File Size: 162,23 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