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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 SzirtesNeural Computation|May 20, 2005
Asymptotic theory of information-theoretic experimental designLiam PaninskiNeural Computation|September 27, 2006
The spike-triggered average of the integrate-and-fire cell driven by gaussian white noiseLiam PaninskiJournal of Computational Neuroscience|November 28, 2009
Fast Kalman filtering on quasilinear dendritic treesLiam PaninskiJournal of Computational Neuroscience|April 25, 2006
The most likely voltage path and large deviations approximations for integrate-and-fire neuronsLiam PaninskiNetwork (Bristol, England)|December 17, 2004
Maximum likelihood estimation of cascade point-process neural encoding modelsLiam PaninskiNetwork (Bristol, England)|August 27, 2003
Convergence properties of three spike-triggered analysis techniquesLiam PaninskiJournal 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 PaninskiNetwork (Bristol, England)|June 8, 2013
Computing loss of efficiency in optimal Bayesian decoders given noisy or incomplete spike trainsCarl Smith, Liam PaninskiIEEE 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 PaninskiPageof 15