DocumentCode
3311098
Title
Finding and analyzing likely models that describe neural population responses
Author
Johnson, Don H. ; Uppuluri, Jyotirmai
Author_Institution
Dept. of Electr. & Comput. Eng., Rice Univ., Houston, TX, USA
fYear
2004
fDate
1-4 Aug. 2004
Firstpage
311
Lastpage
314
Abstract
In this paper, we outline a manner in which we determine likely models that can describe a given set of neural population response data. We then use this information regarding the likelihood of all possible models to determine the Kullback-Leibler distance between neural population responses to different stimulus conditions.
Keywords
maximum likelihood estimation; medical signal processing; neural nets; Kullback-Leibler distance; maximum likelihood estimation; neural population response modeling; neural population statistics; stimulus conditions; stochastic stimuli; Context modeling; Data analysis; Data engineering; Information theory; Maximum likelihood estimation; Neurons; Parameter estimation; Parametric statistics; Pattern analysis; Stochastic processes;
fLanguage
English
Publisher
ieee
Conference_Titel
Digital Signal Processing Workshop, 2004 and the 3rd IEEE Signal Processing Education Workshop. 2004 IEEE 11th
Print_ISBN
0-7803-8434-2
Type
conf
DOI
10.1109/DSPWS.2004.1437965
Filename
1437965
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