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Neural Computation|October 23, 2010
Efficient Markov chain Monte Carlo methods for decoding neural spike trainsYashar Ahmadian, Jonathan W Pillow, Liam PaninskiNetwork (Bristol, England)|February 27, 2008
Inferring input nonlinearities in neural encoding modelsMisha B Ahrens, Liam Paninski, Maneesh SahaniNeural Computation|September 1, 2009
Mean-field approximations for coupled populations of generalized linear model spiking neurons with Markov refractorinessTaro Toyoizumi, Kamiar Rahnama Rad, Liam PaninskiExperimental Brain Research|April 5, 2003
Sequential movement representations based on correlated neuronal activityNicholas G Hatsopoulos, Liam Paninski, John P DonoghuePlos 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 SimoncelliJournal of Neurophysiology|June 23, 2006
Linear encoding of muscle activity in primary motor cortex and cerebellumBenjamin R Townsend, Liam Paninski, Roger N LemonJournal of Neuroscience Methods|April 3, 2010
Population decoding of motor cortical activity using a generalized linear model with hidden statesVernon Lawhern, Wei Wu, Nicholas Hatsopoulos, et al.Neural Computation|February 9, 2011
Hidden Markov models for the stimulus-response relationships of multistate neural systemsSean Escola, Alfredo Fontanini, Don Katz, et al.Journal of Computational Neuroscience|October 1, 2013
Fast state-space methods for inferring dendritic synaptic connectivityAri Pakman, Jonathan Huggins, Carl Smith, et al.Pageof 15