Maneesh Sahani

Teresa H. Meng, Maneesh Sahani, Caleb Kemere, 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning

Maneesh Sahani, M. Sahani, 2003, NIPS.

Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity

Peter Dayan, Maneesh Sahani, P. Dayan, 2003, Neural Computation.

Models of Neuronal Stimulus-Response Functions: Elaboration, Estimation, and Evaluation

Maneesh Sahani, R. S. Williamson, Arne F. Meyer, 2017, Front. Syst. Neurosci..

A Universal Marginalizer for Amortized Inference in Generative Models pdf

Yura N. Perov, Maneesh Sahani, Adam Baker, 2017, ArXiv.

How Linear are Auditory Cortical Responses?

Maneesh Sahani, Jennifer F. Linden, M. Sahani, 2002, NIPS.

A neurally plausible model for online recognition and postdiction

Maneesh Sahani, Li Kevin Wenliang, M. Sahani, 2019, bioRxiv.

Dynamical segmentation of single trials from population neural data

John P. Cunningham, Byron M. Yu, Gopal Santhanam, 2011, NIPS.

Mixture of Trajectory Models for Neural Decoding of Goal-directed Movements a Computational Model of Craving and Obsession Decoding Visual Inputs from Multiple Neurons in the Human Temporal Lobe Encoding Contribution of Individual Retinal Ganglion Cell Responses to Velocity and Acceleration

Byron M. Yu, Afsheen Afshar, Gopal Santhanam, 2008 .

Outlier Responses Reflect Sensitivity to Statistical Structure in the Human Brain

Raymond J. Dolan, Marta I. Garrido, Maneesh Sahani, 2013, PLoS Comput. Biol..

Occlusive Components Analysis

Richard E. Turner, Jörg Lücke, Maneesh Sahani, 2009, NIPS.

Spike train coherence in macaque parietal cortex during a memory saccade task

Richard A. Andersen, Maneesh Sahani, John S. Pezaris, 2000, Neurocomputing.

Time-Frequency Analysis as Probabilistic Inference

Richard E. Turner, Maneesh Sahani, M. Sahani, 2014, IEEE Transactions on Signal Processing.

Equating information-theoretic and likelihood-based methods for neural dimensionality reduction

Maneesh Sahani, Jonathan W. Pillow, R. S. Williamson, 2013, 1308.3542.

Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms

Maneesh Sahani, John S. Pezaris, Michael Wehr, 1999, Neurocomputing.

Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex.

Christoph E Schreiner, Maneesh Sahani, Robert C. Liu, 2003, Journal of neurophysiology.

Generalized Softmax Networks for Non-linear Component Extraction

Jörg Lücke, Maneesh Sahani, M. Sahani, 2007, ICANN.

Evidence for smooth and inertial dynamics in the evolution of neural state

Byron M. Yu, Afsheen Afshar, Gopal Santhanam, 2008 .

Attention in a Bayesian Framework

Maneesh Sahani, Louise Whiteley, M. Sahani, 2012, Front. Hum. Neurosci..

Expectation Propagation for Inference in Non-Linear Dynamical Models with Poisson Observations

Byron M. Yu, Maneesh Sahani, Krishna V Shenoy, 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

A Maximum-Likelihood Interpretation for Slow Feature Analysis

Richard E. Turner, Maneesh Sahani, M. Sahani, 2007, Neural Computation.

An ultralight head-mounted camera system integrates detailed behavioral monitoring with multichannel electrophysiology in freely moving mice

Maneesh Sahani, Jennifer F. Linden, John O'Keefe, 2018, bioRxiv.

Unsupervised learning

Peter Dayan, Maneesh Sahani, Gregoire Deback, 1999 .

Latent variable models for neural data analysis

Richard A. Andersen, Maneesh Sahani, R. Andersen, 1999 .

Efficient occlusive components analysis

Richard E. Turner, Julian Eggert, Jörg Lücke, 2014, J. Mach. Learn. Res..

Regularization and nonlinearities for neural language models: when are they needed? pdf

Maneesh Sahani, Marius Pachitariu, M. Sahani, 2013, ArXiv.

Input-Specific Gain Modulation by Local Sensory Context Shapes Cortical and Thalamic Responses to Complex Sounds

Maneesh Sahani, Misha B. Ahrens, R. S. Williamson, 2016, Neuron.

Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models

John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, 2007, ICONIP.

Bayesian Manifold Learning: Locally Linear Latent Variable Model (LL-LVM): poster

Mijung Park, Wittawat Jitkrittum, Maneesh Sahani, 2014 .

Kernel Instrumental Variable Regression

Arthur Gretton, Maneesh Sahani, Rahul Singh, 2019, NeurIPS.

Recurrent linear models of simultaneously-recorded neural populations

Maneesh Sahani, Marius Pachitariu, Biljana Petreska, 2013, NIPS.

Reconstructing MEG Sources with Unknown Correlations

Srikantan S. Nagarajan, Maneesh Sahani, M. Sahani, 2003, NIPS.

Probabilistic amplitude and frequency demodulation

Richard E. Turner, Maneesh Sahani, M. Sahani, 2011, NIPS.

Learning interpretable continuous-time models of latent stochastic dynamical systems pdf

Maneesh Sahani, Lea Duncker, Gergo Bohner, 2019, ICML.

Factor-analysis methods for higher-performance neural prostheses.

Byron M. Yu, Afsheen Afshar, Gopal Santhanam, 2009, Journal of neurophysiology.

On Sparsity and Overcompleteness in Image Models

Richard E. Turner, Maneesh Sahani, Pietro Berkes, 2007, NIPS.

Neural Decoding for Motor and Communication Prostheses

Byron M. Yu, Gopal Santhanam, Maneesh Sahani, 2010 .

Flexible and accurate inference and learning for deep generative models

Maneesh Sahani, Eszter Vértes, M. Sahani, 2018, NeurIPS.

Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation

Richard E. Turner, Maneesh Sahani, M. Sahani, 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

A Head-Mounted Camera System Integrates Detailed Behavioral Monitoring with Multichannel Electrophysiology in Freely Moving Mice

Maneesh Sahani, Arne F. Meyer, Jennifer F. Linden, 2018, Neuron.

Empirical fixed point bifurcation analysis pdf

Maneesh Sahani, Gergo Bohner, M. Sahani, 2018, ArXiv.

Inhibitory control of correlated intrinsic variability in cortical networks

Maneesh Sahani, Nicholas Lesica, Marius Pachitariu, 2016 .

Empirical models of spiking in neural populations

John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, 2011, NIPS.

Probabilistic Amplitude Demodulation

Richard E. Turner, Maneesh Sahani, 2007, ICA.

Score-matching estimators for continuous-time point-process regression models

Maneesh Sahani, Arne Meyer, Gergo Bohner, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

On the Separation of Signals from Neighboring Cells in Tetrode Recordings

Richard A. Andersen, Maneesh Sahani, John S. Pezaris, 1997, NIPS.

Modeling Natural Sounds with Modulation Cascade Processes

Richard E. Turner, Maneesh Sahani, M. Sahani, 2007, NIPS.

Prior context in audition informs binding and shapes simple features

Maneesh Sahani, Daniel Pressnitzer, Shihab Shamma, 2017, Nature Communications.

Evidence Optimization Techniques for Estimating Stimulus-Response Functions

Maneesh Sahani, Jennifer F. Linden, M. Sahani, 2002, NIPS.

Modelling the dynamics of integrating context into perception: in good and in poor readers

Merav Ahissar, Maneesh Sahani, Vincent Adam, 2017, CogSci.

Convolutional higher order matching pursuit

Maneesh Sahani, Gergo Bohner, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference

Richard E. Turner, Maneesh Sahani, M. Sahani, 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes

John P. Cunningham, Byron M. Yu, Krishna V. Shenoy, 2007, NIPS.

Extracting Dynamical Structure Embedded in Motor Preparatory Activity

Byron M. Yu, Afsheen Afshar, Gopal Santhanam, 2005 .

Inferring Elapsed Time from Stochastic Neural Processes

Maneesh Sahani, Misha Ahrens, M. Sahani, 2007, NIPS.

A neurally plausible model learns successor representations in partially observable environments

Maneesh Sahani, Eszter Vértes, M. Sahani, 2019, NeurIPS.

Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference

James Hensman, Maneesh Sahani, Vincent Adam, 2016, 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP).

A maximum likelihood algorithm for slow features analysis

Richard E. Turner, Maneesh Sahani, 2005 .

Response-locked changes in auto- and cross-covariations in parietal cortex

Richard A. Andersen, Maneesh Sahani, John S. Pezaris, 1999, Neurocomputing.

Fast Gaussian process methods for point process intensity estimation

John P. Cunningham, Krishna V. Shenoy, Maneesh Sahani, 2008, ICML '08.

Equating information-theoretic and likelihood-based methods for neural dimensionality reduction

Maneesh Sahani, Jonathan W. Pillow, Ross S. Williamson, 2013 .

Learning visual motion in recurrent neural networks

Maneesh Sahani, Marius Pachitariu, M. Sahani, 2012, NIPS.

Bayesian perception of the pitch of non-stationary natural sounds

Maneesh Sahani, Adam Vincent, 2014 .

Extracting regions of interest from biological images with convolutional sparse block coding

Adam M. Packer, Maneesh Sahani, Marius Pachitariu, 2013, NIPS.

Maximal Causes for Non-linear Component Extraction

Jörg Lücke, Maneesh Sahani, M. Sahani, 2008, J. Mach. Learn. Res..

A First-Occupancy Representation for Reinforcement Learning pdf

Maneesh Sahani, Theodore H. Moskovitz, Ted Moskovitz, 2021, ICLR.

Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning pdf

Maneesh Sahani, Josh Merel, Grace W. Lindsay, 2021, ArXiv.

Learning Shapes Cortical Dynamics to Enhance Integration of Relevant Sensory Input

Maneesh Sahani, Angus Chadwick, Jasper Poort, 2021, SSRN Electronic Journal.

Five key factors determining pairwise correlations in visual cortex

Matteo Carandini, Maneesh Sahani, M. Carandini, 2015, Journal of neurophysiology.

Amortised Learning by Wake-Sleep

Maneesh Sahani, Heishiro Kanagawa, Theodore Moskovitz, 2020, ICML.

The need for calcium imaging in nonhuman primates: New motor neuroscience and brain-machine interfaces

Krishna V. Shenoy, Maneesh Sahani, Karl Deisseroth, 2017, Experimental Neurology.

Surprise leads to noisier perceptual decisions

Maneesh Sahani, Marta I Garrido, M. Sahani, 2011, i-Perception.

Inhibitory control of correlated intrinsic variability in cortical networks

Kenneth D Harris, Maneesh Sahani, Michael Okun, 2016, bioRxiv.

Extracellular recording from adjacent neurons: II. Correlations in macaque parietal cortex

Richard A. Andersen, Maneesh Sahani, Js Pezaris, 1997 .

Factor Analysis with Poisson Output

Gopal Santhanam, Krishna V. Shenoy, Maneesh Sahani, 2006 .

Functional Evidence for a Dual Route to Amygdala

Marta I. Garrido, Gareth R. Barnes, Maneesh Sahani, 2012, Current Biology.