Search results for "Liam Paninski"

Hits ?▲	Authors	Title	Venue	Year	Link	Author keywords
1	Jonathan Hunter Huggins, Liam Paninski	Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime.	Journal of Computational Neuroscience	2012	DBLP DOI BibTeX RDF
1	Eftychios A. Pnevmatikakis, Liam Paninski	Fast interior-point inference in high-dimensional sparse, penalized state-space models.	Journal of Machine Learning Research - Proceedings Track	2012	DBLP BibTeX RDF
1	Carl Smith, Frank Wood, Liam Paninski	Low rank continuous-space graphical models.	Journal of Machine Learning Research - Proceedings Track	2012	DBLP BibTeX RDF
1	Jeremy Lewi, David M. Schneider, Sarah M. N. Woolley, Liam Paninski	Automating the design of informative sequences of sensory stimuli.	Journal of Computational Neuroscience	2011	DBLP DOI BibTeX RDF
1	Yashar Ahmadian, Jonathan Pillow, Liam Paninski	Efficient Markov Chain Monte Carlo Methods for Decoding Neural Spike Trains.	Neural Computation	2011	DBLP DOI BibTeX RDF
1	Sean Escola, Alfredo Fontanini, Don Katz, Liam Paninski	Hidden Markov Models for the Stimulus-Response Relationships of Multistate Neural Systems.	Neural Computation	2011	DBLP DOI BibTeX RDF
1	Jonathan Pillow, Yashar Ahmadian, Liam Paninski	Model-Based Decoding, Information Estimation, and Change-Point Detection Techniques for Multineuron Spike Trains.	Neural Computation	2011	DBLP DOI BibTeX RDF
1	Kamiar Rahnama Rad, Liam Paninski	Information Rates and Optimal Decoding in Large Neural Populations.	NIPS	2011	DBLP BibTeX RDF
1	Liam Paninski, Yashar Ahmadian, Daniel Gil Ferreira, Shinsuke Koyama, Kamiar Rahnama Rad, Michael Vidne, Joshua T. Vogelstein, Wei Wu	A new look at state-space models for neural data.	Journal of Computational Neuroscience	2010	DBLP DOI BibTeX RDF
1	Shinsuke Koyama, Liam Paninski	Efficient computation of the maximum a posteriori path and parameter estimation in integrate-and-fire and more general state-space models.	Journal of Computational Neuroscience	2010	DBLP DOI BibTeX RDF
1	Liam Paninski	Fast Kalman filtering on quasilinear dendritic trees.	Journal of Computational Neuroscience	2010	DBLP DOI BibTeX RDF
1	Quentin J. M. Huys, Liam Paninski	Smoothing of, and Parameter Estimation from, Noisy Biophysical Recordings.	PLoS Computational Biology	2009	DBLP DOI BibTeX RDF
1	Sean Escola, Michael Eisele, Kenneth D. Miller, Liam Paninski	Maximally Reliable Markov Chains Under Energy Constraints.	Neural Computation	2009	DBLP DOI BibTeX RDF
1	Taro Toyoizumi, Kamiar Rahnama Rad, Liam Paninski	Mean-Field Approximations for Coupled Populations of Generalized Linear Model Spiking Neurons with Markov Refractoriness.	Neural Computation	2009	DBLP DOI BibTeX RDF
1	Jeremy Lewi, Robert J. Butera, Liam Paninski	Sequential Optimal Design of Neurophysiology Experiments.	Neural Computation	2009	DBLP DOI BibTeX RDF
1	Geoff Fudenberg, Liam Paninski	Bayesian Image Recovery for Dendritic Structures Under Low Signal-to-Noise Conditions.	IEEE Transactions on Image Processing	2009	DBLP DOI BibTeX RDF
1	Liam Paninski, Adrian Haith, Gabor Szirtes	Integral equation methods for computing likelihoods and their derivatives in the stochastic integrate-and-fire model.	Journal of Computational Neuroscience	2008	DBLP DOI BibTeX RDF	Large deviations approximation, Markov process, Volterra integral equation
1	Liam Paninski, Masanao Yajima	Undersmoothed Kernel Entropy Estimators.	IEEE Transactions on Information Theory	2008	DBLP DOI BibTeX RDF
1	Liam Paninski	A Coincidence-Based Test for Uniformity Given Very Sparsely Sampled Discrete Data.	IEEE Transactions on Information Theory	2008	DBLP DOI BibTeX RDF
1	Jeremy Lewi, Robert J. Butera, David M. Schneider, Sarah M. N. Woolley, Liam Paninski	Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds.	NIPS	2008	DBLP BibTeX RDF
1	Jeremy Lewi, Robert J. Butera, Liam Paninski	Efficient active learning with generalized linear models.	Journal of Machine Learning Research - Proceedings Track	2007	DBLP BibTeX RDF
1	Liam Paninski	The most likely voltage path and large deviations approximations for integrate-and-fire neurons.	Journal of Computational Neuroscience	2006	DBLP DOI BibTeX RDF	Stochastic dynamics, Freidlin-Wentzell, Intracellular recordings, Calculus of variations, Likelihood
1	Liam Paninski	The Spike-Triggered Average of the Integrate-and-Fire Cell Driven by Gaussian White Noise.	Neural Computation	2006	DBLP DOI BibTeX RDF
1	Jeremy Lewi, Robert J. Butera, Liam Paninski	Real-time adaptive information-theoretic optimization of neurophysiology experiments.	NIPS	2006	DBLP BibTeX RDF
1	Liam Paninski	Asymptotic Theory of Information-Theoretic Experimental Design.	Neural Computation	2005	DBLP DOI BibTeX RDF
1	Liam Paninski, Jonathan Pillow, Eero P. Simoncelli	Comparing integrate-and-fire models estimated using intracellular and extracellular data.	Neurocomputing	2005	DBLP DOI BibTeX RDF
1	Liam Paninski	Nonparametric inference of prior probabilities from Bayes-optimal behavior.	NIPS	2005	DBLP BibTeX RDF
1	Misha Ahrens, Quentin J. M. Huys, Liam Paninski	Large-scale biophysical parameter estimation in single neurons via constrained linear regression.	NIPS	2005	DBLP BibTeX RDF
1	Liam Paninski, Jonathan Pillow, Eero P. Simoncelli	Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Encoding Model.	Neural Computation	2004	DBLP DOI BibTeX RDF
1	Liam Paninski	Estimating Entropy on m Bins Given Fewer than m Samples.	IEEE Transactions on Information Theory	2004	DBLP DOI BibTeX RDF
1	Liam Paninski	Variational Minimax Estimation of Discrete Distributions under KL Loss.	NIPS	2004	DBLP BibTeX RDF
1	Liam Paninski	Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning.	NIPS	2004	DBLP BibTeX RDF
1	Mijail Serruya, Nicholas G. Hatsopoulos, Matthew Fellows, Liam Paninski, John Donoghue	Robustness of neuroprosthetic decoding algorithms.	Biological Cybernetics	2003	DBLP DOI BibTeX RDF
1	Liam Paninski	Estimation of Entropy and Mutual Information.	Neural Computation	2003	DBLP DOI BibTeX RDF
1	Liam Paninski	Design of Experiments via Information Theory.	NIPS	2003	DBLP BibTeX RDF
1	Jonathan Pillow, Liam Paninski, Eero P. Simoncelli	Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model.	NIPS	2003	DBLP BibTeX RDF
1	Liam Paninski	Convergence Properties of Some Spike-Triggered Analysis Techniques.	NIPS	2002	DBLP BibTeX RDF
1	Liam Paninski, Michael J. Hawken	Stochastic optimal control and the human oculomotor system.	Neurocomputing	2001	DBLP DOI BibTeX RDF

Publications of "Liam Paninski" ( http://dblp.L3S.de/Authors/Liam_Paninski )