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Liam Paninski

Showing results (1-10 of 138) with videos related to

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Neural Computation|May 20, 2005
Asymptotic theory of information-theoretic experimental designLiam Paninski
Neural Computation|September 27, 2006
The spike-triggered average of the integrate-and-fire cell driven by gaussian white noiseLiam Paninski
Journal of Computational Neuroscience|November 28, 2009
Fast Kalman filtering on quasilinear dendritic treesLiam Paninski
Journal of Computational Neuroscience|April 25, 2006
The most likely voltage path and large deviations approximations for integrate-and-fire neuronsLiam Paninski
Network (Bristol, England)|December 17, 2004
Maximum likelihood estimation of cascade point-process neural encoding modelsLiam Paninski
Network (Bristol, England)|August 27, 2003
Convergence properties of three spike-triggered analysis techniquesLiam Paninski
Journal of Computational Neuroscience|April 29, 2009
Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space modelsShinsuke Koyama, Liam Paninski
Network (Bristol, England)|June 8, 2013
Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trainsCarl Smith, Liam Paninski
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|February 13, 2009
Bayesian image recovery for dendritic structures under low signal-to-noise conditionsGeoffrey Fudenberg, Liam Paninski
Neural Computation|September 24, 2014
On quadrature methods for refractory point process likelihoodsGonzalo Mena, Liam Paninski
Pageof 14

Showing results (1-10 of 138) with videos related to

Sort By:
Pageof 14
Neural Computation|May 20, 2005
Asymptotic theory of information-theoretic experimental designLiam Paninski
Neural Computation|September 27, 2006
The spike-triggered average of the integrate-and-fire cell driven by gaussian white noiseLiam Paninski
Journal of Computational Neuroscience|November 28, 2009
Fast Kalman filtering on quasilinear dendritic treesLiam Paninski
Journal of Computational Neuroscience|April 25, 2006
The most likely voltage path and large deviations approximations for integrate-and-fire neuronsLiam Paninski
Network (Bristol, England)|December 17, 2004
Maximum likelihood estimation of cascade point-process neural encoding modelsLiam Paninski
Network (Bristol, England)|August 27, 2003
Convergence properties of three spike-triggered analysis techniquesLiam Paninski
Journal of Computational Neuroscience|April 29, 2009
Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space modelsShinsuke Koyama, Liam Paninski
Network (Bristol, England)|June 8, 2013
Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trainsCarl Smith, Liam Paninski
IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society|February 13, 2009
Bayesian image recovery for dendritic structures under low signal-to-noise conditionsGeoffrey Fudenberg, Liam Paninski
Neural Computation|September 24, 2014
On quadrature methods for refractory point process likelihoodsGonzalo Mena, Liam Paninski
Pageof 14