Neuroscience

Empirical models of spiking in neural populations NeurIPS 2011 Energetically Optimal Action Potentials NeurIPS 2011 Estimating time-varying input signals and ion channel states from a single voltage trace of a neuron NeurIPS 2011 From Stochastic Nonlinear Integrate-and-Fire to Generalized Linear Models NeurIPS 2011 How biased are maximum entropy models? NeurIPS 2011 Inferring spike-timing-dependent plasticity from spike train data NeurIPS 2011 Information Rates and Optimal Decoding in Large Neural Populations NeurIPS 2011 Multilinear Subspace Regression: An Orthogonal Tensor Decomposition Approach NeurIPS 2011 Neural Reconstruction with Approximate Message Passing (NeuRAMP) NeurIPS 2011 Neuronal Adaptation for Sampling-Based Probabilistic Inference in Perceptual Bistability NeurIPS 2011 On the Analysis of Multi-Channel Neural Spike Data NeurIPS 2011 Predicting response time and error rates in visual search NeurIPS 2011 Probabilistic Modeling of Dependencies Among Visual Short-Term Memory Representations NeurIPS 2011 Sequence learning with hidden units in spiking neural networks NeurIPS 2011 Two is better than one: distinct roles for familiarity and recollection in retrieving palimpsest memories NeurIPS 2011 Unsupervised learning models of primary cortical receptive fields and receptive field plasticity NeurIPS 2011 Variational Learning for Recurrent Spiking Networks NeurIPS 2011 Why The Brain Separates Face Recognition From Object Recognition NeurIPS 2011 A biologically plausible network for the computation of orientation dominance NeurIPS 2010 Accounting for network effects in neuronal responses using L1 regularized point process models NeurIPS 2010 A Novel Kernel for Learning a Neuron Model from Spike Train Data NeurIPS 2010 Attractor Dynamics with Synaptic Depression NeurIPS 2010 A VLSI Implementation of the Adaptive Exponential Integrate-and-Fire Neuron Model NeurIPS 2010 Brain covariance selection: better individual functional connectivity models using population prior NeurIPS 2010 Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations NeurIPS 2010