Show simple item record

dc.contributor.advisor Johnson, Don H.
dc.creatorGoodman, Ilan N.
dc.date.accessioned 2009-06-04T07:00:15Z
dc.date.available 2009-06-04T07:00:15Z
dc.date.issued 2005
dc.identifier.urihttps://hdl.handle.net/1911/17786
dc.description.abstract Neurobiologists recently developed tools to record from large populations of neurons, and early results suggest that neurons interact to encode information jointly. However, traditional statistical analysis techniques are inadequate to elucidate these interactions. This thesis develops two multivariate statistical dependence measures that, unlike traditional measures, encompass all high-order and non-linear interactions. These measures decompose the contributions of distinct subpopulations to the total dependence. Applying the dependence analysis to recordings from the crayfish visual system, I show that neural populations exhibit complex dependencies that vary with the stimulus. Using Fisher information to analyze the effectiveness of population codes, I show that optimal rate coding requires negatively dependent responses. Since positive dependence through overlapping stimulus attributes is an inherent characteristic of many neural systems, such neurons can only achieve the optimal code by cooperating.
dc.format.extent 74 p.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectElectronics
Electrical engineering
Neurosciences
Statistics
dc.title Analyzing statistical dependencies in neural populations
dc.type.genre Thesis
dc.type.material Text
thesis.degree.department Statistics
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Masters
thesis.degree.name Master of Science
dc.identifier.citation Goodman, Ilan N.. "Analyzing statistical dependencies in neural populations." (2005) Master’s Thesis, Rice University. https://hdl.handle.net/1911/17786.


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record