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
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;
Conference_Titel :
Digital Signal Processing Workshop, 2004 and the 3rd IEEE Signal Processing Education Workshop. 2004 IEEE 11th
Print_ISBN :
0-7803-8434-2
DOI :
10.1109/DSPWS.2004.1437965
