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Liam Paninski

Showing results (21-30 of 139) with videos related to

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Plos Computational Biology|March 15, 2017
Fast online deconvolution of calcium imaging dataJohannes Friedrich, Pengcheng Zhou, Liam Paninski
Journal of Computational Neuroscience|May 12, 2007
Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire modelLiam Paninski, Adrian Haith, Gabor Szirtes
Journal of the Optical Society of America. A, Optics, Image Science, and Vision|November 4, 2009
The relationship between optimal and biologically plausible decoding of stimulus velocity in the retinaEdmund C Lalor, Yashar Ahmadian, Liam Paninski
Neural Computation|October 23, 2010
Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trainsJonathan W Pillow, Yashar Ahmadian, Liam Paninski
Neural Computation|October 23, 2010
Efficient Markov chain Monte Carlo methods for decoding neural spike trainsYashar Ahmadian, Jonathan W Pillow, Liam Paninski
Network (Bristol, England)|February 27, 2008
Inferring input nonlinearities in neural encoding modelsMisha B Ahrens, Liam Paninski, Maneesh Sahani
Neural Computation|September 1, 2009
Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractorinessTaro Toyoizumi, Kamiar Rahnama Rad, Liam Paninski
Experimental Brain Research|April 5, 2003
Sequential movement representations based on correlated neuronal activityNicholas G Hatsopoulos, Liam Paninski, John P Donoghue
Plos Computational Biology|April 8, 2022
Blind demixing methods for recovering dense neuronal morphology from barcode imaging dataShuonan Chen, Jackson Loper, Pengcheng Zhou, et al.
Neural Computation|November 2, 2004
Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding modelLiam Paninski, Jonathan W Pillow, Eero P Simoncelli
Pageof 14

Showing results (21-30 of 139) with videos related to

Sort By:
Pageof 14
Plos Computational Biology|March 15, 2017
Fast online deconvolution of calcium imaging dataJohannes Friedrich, Pengcheng Zhou, Liam Paninski
Journal of Computational Neuroscience|May 12, 2007
Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire modelLiam Paninski, Adrian Haith, Gabor Szirtes
Journal of the Optical Society of America. A, Optics, Image Science, and Vision|November 4, 2009
The relationship between optimal and biologically plausible decoding of stimulus velocity in the retinaEdmund C Lalor, Yashar Ahmadian, Liam Paninski
Neural Computation|October 23, 2010
Model-based decoding, information estimation, and change-point detection techniques for multineuron spike trainsJonathan W Pillow, Yashar Ahmadian, Liam Paninski
Neural Computation|October 23, 2010
Efficient Markov chain Monte Carlo methods for decoding neural spike trainsYashar Ahmadian, Jonathan W Pillow, Liam Paninski
Network (Bristol, England)|February 27, 2008
Inferring input nonlinearities in neural encoding modelsMisha B Ahrens, Liam Paninski, Maneesh Sahani
Neural Computation|September 1, 2009
Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractorinessTaro Toyoizumi, Kamiar Rahnama Rad, Liam Paninski
Experimental Brain Research|April 5, 2003
Sequential movement representations based on correlated neuronal activityNicholas G Hatsopoulos, Liam Paninski, John P Donoghue
Plos Computational Biology|April 8, 2022
Blind demixing methods for recovering dense neuronal morphology from barcode imaging dataShuonan Chen, Jackson Loper, Pengcheng Zhou, et al.
Neural Computation|November 2, 2004
Maximum likelihood estimation of a stochastic integrate-and-fire neural encoding modelLiam Paninski, Jonathan W Pillow, Eero P Simoncelli
Pageof 14