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Activity Number:
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118
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Type:
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Invited
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Date/Time:
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Monday, July 30, 2007 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Physical and Engineering Sciences
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| Abstract - #307702 |
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Title:
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Bayesian Curve-Fitting and Functional Data Analysis in Neurophysiology
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Author(s):
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Robert E. Kass*+
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Companies:
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Carnegie Mellon University
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Address:
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Department of Statistics, Pittsburgh, PA, 15213,
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Keywords:
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Abstract:
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One of the most important techniques in learning about the functioning of the brain has involved examining neuronal activity in laboratory animals under varying experimental conditions. Neural information is represented and communicated through series of action potentials, or spike trains, and the central scientific issue in many studies concerns the physiological significance that should be attached to a particular neuron firing pattern in a particular part of the brain. We have formalized specific scientific questions in terms of point process intensity functions, and have used Bayesian methods to fit the point process models to neuronal data. I will very briefly outline some of the substantive problems we are examining and will discuss in some detail our applications involving BARS (Bayesian Adaptive Regression Splines), an approach to generalized nonparametric regression.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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