- Evidence Optimization Techniques for Estimating Stimulus-Response Functions
- Cover: Probabilistic decoding of an arm trajectory from spike trains recorded in motor and premotor cortices using a mixture of trajectory models. Each panel corresponds to a
- STATISTICAL INFERENCE FOR SINGLE-AND MULTI-BAND PROBABILISTIC AMPLITUDE DEMODULATION
- To appear in Advances in Neural Information Processing Systems 15 Evidence Optimization Techniques
- Occlusive Components Analysis Jorg Lucke
- The nal version of this article will appear in Neural Computation, Vol. 15, Issue 10, published by The MIT Press. Doubly Distributional Population Codes
- Current Biology 21, 17, February 8, 2011 2011 Elsevier Ltd All rights reserved DOI 10.1016/j.cub.2010.12.043 Observers Exploit Stochastic Models
- Two problems with variational expectation maximisation for time-series models
- Network: Computation in Neural Systems MarchJune 2010; 21(12): 91124
- 103:3238-3247, 2010. First published Mar 31, 2010; doi:10.1152/jn.01084.2009J Neurophysiol Raymond J. Dolan
- A Structured Model of Video Reproduces Primary Visual Cortical Organisation
- Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods
- Journal of Machine Learning Research 9 (2008) 1227-1267 Submitted 5/07; Revised 11/07; Published 6/08 Maximal Causes for Non-linear Component Extraction
- A FACTOR-ANALYSIS DECODER FOR HIGH-PERFORMANCE NEURAL PROSTHESES G. Santhanam1
- Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models
- Implicit knowledge of visual uncertainty guides decisions with asymmetric outcomes
- Techniques for extracting single-trial activity patterns from large-scale neural recordings
- Generalized Softmax Networks for Non-linear Component Extraction
- Probabilistic Amplitude Demodulation Richard E. Turner and Maneesh Sahani
- Extracting Dynamical Structure Embedded in Neural Activity
- BOOTSTRAP-BASED STATISTICAL THRESHOLDING FOR MEG SOURCE RECONSTRUCTION IMAGES
- Latent Variable Models for Neural Data Analysis Maneesh Sahani
- Inferring input nonlinearities in neural encoding models Misha B. Ahrens1
- Reconstructing MEG Sources with Unknown Correlations
- Modeling Cue Integration in Cluttered Environments
- Inferring Elapsed Time from Stochastic Neural Processes
- Stimulus onset quenches neural variability: a widespread cortical phenomenon
- Latent Variable Models for Neural Data Analysis Maneesh Sahani
- How Linear are Auditory Cortical Responses? Maneesh Sahani
- How Linear are Auditory Cortical Responses? Maneesh Sahani
- EXPECTATION PROPAGATION FOR INFERENCE IN NON-LINEAR DYNAMICAL MODELS WITH POISSON OBSERVATIONS
- A Biologically Plausible Algorithm for Reinforcement-shaped
- To appear in Advances in Neural Information Processing Systems 10, M. I. Jordan, M. J. Kearns, and S. A. Solla, eds., MIT Press, Cambridge, MA (1998).
- Modeling Natural Sounds with Modulation Cascade Processes
- On Sparsity and Overcompleteness in Image Models Pietro Berkes, Richard Turner, and Maneesh Sahani
- An Extensible Infrastructure for Fully Automated Spike Sorting during Online Experiments
- The final version of this article will appear in Neural Computation, Vol. 15, Issue 10, published by The MIT Press. Doubly Distributional Population Codes
- IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, VOL. XX, NO. X, MARCH 2011 1 Demodulation as Probabilistic Inference
- Behavioral/Systems/Cognitive Depth-Dependent Temporal Response Properties in Core
- Petreska et al. Dynamical segmentation of single trials from population neural data NIPS 2011 Pre-conference version Dynamical segmentation of single trials
- Turner and Sahani Probabilistic amplitude and frequency demodulation NIPS 2011 Pre-conference version Probabilistic amplitude and frequency demodulation
- Macke et al. Empirical models of spiking in neural populations NIPS 2011 Pre-conference version Empirical models of spiking in neural populations
