Bayesian decoding of neural signals

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Candidates are listed in the order they were nominated:

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	Name/Email/Affiliation	Brief note
	Liam Paninski (edit) (liamstat.columbia.edu) Columbia	Expert in the field. Written many papers on the subject.
	Emery Brown (edit) (brownneurostat.mgh.harvard.edu) Harvard/MIT	Expert in the field. Written many papers on the subject.
	Jack Gallant (edit) (gallantberkeley.edu) UC Berkeley	He is an expert in the field who has written many influential articles. Recently he has worked on decoding natural images from fMRI data.
	Wei Ji Ma (edit) (wjmabcm.edu) Theoretical Systems Neuroscience Laboratory, Department of Neuroscience, Baylor College of Medicine, Houston, USA	showed Poisson-like variability observed in the cortex can underpin a type of probabilistic population code to implement near-optimal Bayesian inference in a wide variety of tasks. (http://www.nature.com/neuro/journal/v9/n11/abs/nn1790.html)
	Alexandre Pouget (edit) (alexbcs.rochester.edu) University of Rochester	Dr. Pouget is a leading proponent of Bayesian approaches to neural decoding. Has a number of high profile publications in the field.

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