NIPS Proceedingsβ

Liam Paninski

19 Papers

  • Automated scalable segmentation of neurons from multispectral images (2016)
  • Fast Active Set Methods for Online Spike Inference from Calcium Imaging (2016)
  • Linear dynamical neural population models through nonlinear embeddings (2016)
  • Clustered factor analysis of multineuronal spike data (2014)
  • A multi-agent control framework for co-adaptation in brain-computer interfaces (2013)
  • Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions (2013)
  • Bayesian Inference and Online Experimental Design for Mapping Neural Microcircuits (2013)
  • Robust learning of low-dimensional dynamics from large neural ensembles (2013)
  • Sparse nonnegative deconvolution for compressive calcium imaging: algorithms and phase transitions (2013)
  • Information Rates and Optimal Decoding in Large Neural Populations (2011)
  • Designing neurophysiology experiments to optimally constrain receptive field models along parametric submanifolds (2008)
  • Real-time adaptive information-theoretic optimization of neurophysiology experiments (2006)
  • Large-scale biophysical parameter estimation in single neurons via constrained linear regression (2005)
  • Nonparametric inference of prior probabilities from Bayes-optimal behavior (2005)
  • Log-concavity Results on Gaussian Process Methods for Supervised and Unsupervised Learning (2004)
  • Variational Minimax Estimation of Discrete Distributions under KL Loss (2004)
  • Design of Experiments via Information Theory (2003)
  • Maximum Likelihood Estimation of a Stochastic Integrate-and-Fire Neural Model (2003)
  • Convergence Properties of Some Spike-Triggered Analysis Techniques (2002)