Analyzing the robustness of redundant population codes in sensory and feature extraction systems

dc.citation.bibtexNameinproceedingsen_US
dc.citation.conferenceNameComputational Neuroscience Meetingen_US
dc.citation.locationMadison, WIen_US
dc.contributor.authorRozell, Chrisen_US
dc.contributor.authorJohnson, Donen_US
dc.date.accessioned2007-10-31T01:03:15Zen_US
dc.date.available2007-10-31T01:03:15Zen_US
dc.date.issued2005-07-01en_US
dc.date.modified2005-07-06en_US
dc.date.note2005-02-04en_US
dc.date.submitted2005-07-01en_US
dc.descriptionConference paperen_US
dc.description.abstractSensorineural systems often use groups of redundant neurons to represent stimulus information both during transduction and population coding of features. This redundancy makes the system more robust to corruption in the representation. We approximate neural coding as a projection of the stimulus onto a set of vectors, with the result encoded by spike trains. We use the formalism of frame theory to quantify the inherent noise reduction properties of such population codes. Additionally, computing features from the stimulus signal can also be thought of as projecting the coefficients of a sensory representation onto another set of vectors specific to the feature of interest. The conditions under which a combination of different features form a complete representation for the stimulus signal can be found through a recent extension to frame theory called "frames of subspaces". We extend the frame of subspaces theory to quantify the noise reduction properties of a collection of redundant feature spaces.en_US
dc.identifier.citationC. Rozell and D. Johnson, "Analyzing the robustness of redundant population codes in sensory and feature extraction systems," 2005.en_US
dc.identifier.urihttps://hdl.handle.net/1911/20308en_US
dc.language.isoengen_US
dc.titleAnalyzing the robustness of redundant population codes in sensory and feature extraction systemsen_US
dc.typeConference paperen_US
dc.type.dcmiTexten_US
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