Science

Infinite Relational Modeling of Functional Connectivity in Resting State fMRI NeurIPS 2010 Inter-time segment information sharing for non-homogeneous dynamic Bayesian networks NeurIPS 2010 Learning to localise sounds with spiking neural networks NeurIPS 2010 Linear readout from a neural population with partial correlation data NeurIPS 2010 Over-complete representations on recurrent neural networks can support persistent percepts NeurIPS 2010 Penalized Principal Component Regression on Graphs for Analysis of Subnetworks NeurIPS 2010 Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models NeurIPS 2010 Short-term memory in neuronal networks through dynamical compressed sensing NeurIPS 2010 Sodium entry efficiency during action potentials: A novel single-parameter family of Hodgkin-Huxley models NeurIPS 2010 Sparse Instrumental Variables (SPIV) for Genome-Wide Studies NeurIPS 2010 SpikeAnts, a spiking neuron network modelling the emergence of organization in a complex system NeurIPS 2010 Spike timing-dependent plasticity as dynamic filter NeurIPS 2010 Switching state space model for simultaneously estimating state transitions and nonstationary firing rates NeurIPS 2010 The Neural Costs of Optimal Control NeurIPS 2010 Variational Inference over Combinatorial Spaces NeurIPS 2010 A Biologically Plausible Model for Rapid Natural Scene Identification NeurIPS 2009 A joint maximum-entropy model for binary neural population patterns and continuous signals NeurIPS 2009 A Neural Implementation of the Kalman Filter NeurIPS 2009 Augmenting Feature-driven fMRI Analyses: Semi-supervised learning and resting state activity NeurIPS 2009 Bayesian estimation of orientation preference maps NeurIPS 2009 Bayesian Source Localization with the Multivariate Laplace Prior NeurIPS 2009 Boosting with Spatial Regularization NeurIPS 2009 Clustering sequence sets for motif discovery NeurIPS 2009 Code-specific policy gradient rules for spiking neurons NeurIPS 2009 Correlation Coefficients are Insufficient for Analyzing Spike Count Dependencies NeurIPS 2009