This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract - #308043 |
Title:
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Identification of Neural Functional Connectivity Using a Sparse Generalized Volterra Model
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Author(s):
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Dong Song*+ and Theodore W. Berger
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Companies:
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University of Southern California and University of Southern California
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Address:
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, , ,
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Keywords:
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spike ;
GLM ;
L1 regularization ;
Volterra kernel
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Abstract:
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To understand how a brain region processes information, it is essential to identify the functional connectivity between populations of neurons from their spiking activity. We formulate a physiologically-plausible neuron model that consists of a set of feedforward Volterra kernels, a feedback Volterra kernel, a Gaussian noise, and a threshold. This model is equivalent to a cascade of a multiple-input Volterra model and a probit link function and thus can be termed as a generalized Volterra model (GVM). GVMs are estimated with a group L1-regularization method that results in sparse GVMs. A sparse GVM includes only the significant groups of coefficients corresponding to the significant input-output functional connections and their orders of nonlinearities, and thus provides a quantitative representation of the neural functional connectivity.
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Authors who are presenting talks have a * after their name.
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