Clustered factor analysis of multineuronal spike data Lars Buesing1 , Timothy A. Machado1,2 , John P. Cunningham1 and Liam Paninski1 1 Department of Statistics, Center for Theoretical Neuroscience - Artificial neural network - Document - PDFSEARCH.IO

First Page		Document Content
Date: 2014-10-13 19:51:15 Statistics Applied mathematics Machine learning Cluster analysis Cybernetics Dimension reduction Computational neuroscience Mixture model K-means clustering Expectationmaximization algorithm Principal component analysis Artificial neural network		Clustered factor analysis of multineuronal spike data Lars Buesing1 , Timothy A. Machado1,2 , John P. Cunningham1 and Liam Paninski1 1 Department of Statistics, Center for Theoretical Neuroscience & Grossman Center for Add to Reading List Source URL: www.gatsby.ucl.ac.uk Download Document from Source Website File Size: 416,31 KB Share Document on Facebook

	Newton Method for the ICA Mixture Model DocID: 1vftd - View Document
	Semi-Supervised Learning with the Deep Rendering Mixture Model Tan Nguyen1,2 Wanjia Liu1 Ethan Perez1 Richard G. Baraniuk1 DocID: 1v26R - View Document
	BUDVYTIS ET AL.: MOT FOR VIDEO SEGMENTATION 1 MoT - Mixture of Trees Probabilistic Graphical Model for Video Segmentation DocID: 1uILx - View Document
	Vol. 18 noPages 1194–1206 BIOINFORMATICS Bayesian infinite mixture model based clustering DocID: 1usTr - View Document
	Research © 2011 by The American Society for Biochemistry and Molecular Biology, Inc. This paper is available on line at http://www.mcponline.org A Bayesian Mixture Model for Comparative Spectral Count Data in Shotgun Pr DocID: 1uaWx - View Document