On the Separation of Signals from Neighboring Cells in Tetrode Recordings. - Maneesh Sahani; John S. Pezaris; Richard A. Andersen (NIPS, 1997)

Response-locked changes in auto- and cross-covariations in parietal cortex. - John S. Pezaris; Maneesh Sahani; Richard A. Andersen (Neurocomputing, 1999)

Simultaneous paired intracellular and tetrode recordings for evaluating the performance of spike sorting algorithms. - Michael Wehr; John S. Pezaris; Maneesh Sahani (Neurocomputing, 1999)

Spike train coherence in macaque parietal cortex during a memory saccade task. - John S. Pezaris; Maneesh Sahani; Richard A. Andersen (Neurocomputing, 2000)

Adaptation and Unsupervised Learning. - Peter Dayan; Maneesh Sahani; Gregoire Deback (NIPS, 2002)

How Linear are Auditory Cortical Responses?. - Maneesh Sahani; Jennifer F. Linden (NIPS, 2002)

Evidence Optimization Techniques for Estimating Stimulus-Response Functions. - Maneesh Sahani; Jennifer F. Linden (NIPS, 2002)

A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning. - Maneesh Sahani (NIPS, 2003)

Reconstructing MEG Sources with Unknown Correlations. - Maneesh Sahani; Srikantan S. Nagarajan (NIPS, 2003)

Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity. - Maneesh Sahani; Peter Dayan (Neural Comput., 2003)

Extracting Dynamical Structure Embedded in Neural Activity. - Byron M. Yu; Afsheen Afshar; Gopal Santhanam; Stephen I. Ryu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2005)

A simple nonparametric statistical thresholding for MEG spatial-filter source reconstruction images. - Kensuke Sekihara; Maneesh Sahani; Srikantan S. Nagarajan (NeuroImage, 2005)

Localization bias and spatial resolution of adaptive and non-adaptive spatial filters for MEG source reconstruction. - Kensuke Sekihara; Maneesh Sahani; Srikantan S. Nagarajan (NeuroImage, 2005)

Probabilistic Amplitude Demodulation. - Richard E. Turner; Maneesh Sahani (ICA, 2007)

Generalized Softmax Networks for Non-linear Component Extraction. - Jörg Lücke; Maneesh Sahani (ICANN (1), 2007)

Latent Identity Variables: Biometric Matching Without Explicit Identity Estimation. - Simon J. D. Prince; Jania Aghajanian; Umar Mohammed; Maneesh Sahani (ICB, 2007)

Neural Decoding of Movements: From Linear to Nonlinear Trajectory Models. - Byron M. Yu; John P. Cunningham; Krishna V. Shenoy; Maneesh Sahani (ICONIP (1), 2007)

Inferring Elapsed Time from Stochastic Neural Processes. - Misha B. Ahrens; Maneesh Sahani (NIPS, 2007)

On Sparsity and Overcompleteness in Image Models. - Pietro Berkes; Richard E. Turner; Maneesh Sahani (NIPS, 2007)

Inferring Neural Firing Rates from Spike Trains Using Gaussian Processes. - John P. Cunningham; Byron M. Yu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2007)

Modeling Natural Sounds with Modulation Cascade Processes. - Richard E. Turner; Maneesh Sahani (NIPS, 2007)

A Maximum-Likelihood Interpretation for Slow Feature Analysis. - Richard E. Turner; Maneesh Sahani (Neural Comput., 2007)

A factor-analysis decoder for high-performance neural prostheses. - Gopal Santhanam; Byron M. Yu; Vikash Gilja; Stephen I. Ryu; Afsheen Afshar; Maneesh Sahani; Krishna V. Shenoy (ICASSP, 2008)

Fast Gaussian process methods for point process intensity estimation. - John P. Cunningham; Krishna V. Shenoy; Maneesh Sahani (ICML, 2008)

Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity. - Byron M. Yu; John P. Cunningham; Gopal Santhanam; Stephen I. Ryu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2008)

Maximal Causes for Non-linear Component Extraction. - Jörg Lücke; Maneesh Sahani (J. Mach. Learn. Res., 2008)

Occlusive Components Analysis. - Jörg Lücke; Richard E. Turner; Maneesh Sahani; Marc Henniges (NIPS, 2009)

A Structured Model of Video Reproduces Primary Visual Cortical Organisation. - Pietro Berkes; Richard E. Turner; Maneesh Sahani (PLoS Comput. Biol., 2009)

Statistical inference for single- and multi-band Probabilistic Amplitude Demodulation. - Richard E. Turner; Maneesh Sahani (ICASSP, 2010)

Empirical models of spiking in neural populations. - Jakob H. Macke; Lars Buesing; John P. Cunningham; Byron M. Yu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2011)

Dynamical segmentation of single trials from population neural data. - Biljana Petreska; Byron M. Yu; John P. Cunningham; Gopal Santhanam; Stephen I. Ryu; Krishna V. Shenoy; Maneesh Sahani (NIPS, 2011)

Probabilistic amplitude and frequency demodulation. - Richard E. Turner; Maneesh Sahani (NIPS, 2011)

Demodulation as Probabilistic Inference. - Richard E. Turner; Maneesh Sahani (IEEE ACM Trans. Audio Speech Lang. Process., 2011)

Decomposing signals into a sum of amplitude and frequency modulated sinusoids using probabilistic inference. - Richard E. Turner; Maneesh Sahani (ICASSP, 2012)

Variational Inference in Non-negative Factorial Hidden Markov Models for Efficient Audio Source Separation. - Gautham J. Mysore; Maneesh Sahani (ICML, 2012)

Spectral learning of linear dynamics from generalised-linear observations with application to neural population data. - Lars Buesing; Jakob H. Macke; Maneesh Sahani (NIPS, 2012)

Learning visual motion in recurrent neural networks. - Marius Pachitariu; Maneesh Sahani (NIPS, 2012)

Extracting regions of interest from biological images with convolutional sparse block coding. - Marius Pachitariu; Adam M. Packer; Noah Pettit; Henry Dalgleish; Michael Häusser; Maneesh Sahani (NIPS, 2013)

Recurrent linear models of simultaneously-recorded neural populations. - Marius Pachitariu; Biljana Petreska; Maneesh Sahani (NIPS, 2013)

Regularization and nonlinearities for neural language models: when are they needed? - Marius Pachitariu; Maneesh Sahani (CoRR, 2013)

Outlier Responses Reflect Sensitivity to Statistical Structure in the Human Brain. - Marta I. Garrido; Maneesh Sahani; Raymond J. Dolan (PLoS Comput. Biol., 2013)

Efficient occlusive components analysis. - Marc Henniges; Richard E. Turner; Maneesh Sahani; Julian Eggert; Jörg Lücke (J. Mach. Learn. Res., 2014)

Time-Frequency Analysis as Probabilistic Inference. - Richard E. Turner; Maneesh Sahani (IEEE Trans. Signal Process., 2014)

Bayesian Manifold Learning: The Locally Linear Latent Variable Model (LL-LVM). - Mijung Park; Wittawat Jitkrittum; Ahmad Qamar; Zoltán Szabó 001; Lars Buesing; Maneesh Sahani (NIPS, 2015)

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction. - Ross S. Williamson; Maneesh Sahani; Jonathan W. Pillow (PLoS Comput. Biol., 2015)

Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference. - Vincent Adam; James Hensman; Maneesh Sahani (MLSP, 2016)

Convolutional higher order matching pursuit. - Gergo Bohner; Maneesh Sahani (MLSP, 2016)

Score-matching estimators for continuous-time point-process regression models. - Maneesh Sahani; Gergo Bohner; Arne Meyer (MLSP, 2016)

Modelling the dynamics of integrating context into perception: in good and in poor readers. - Itay Lieder; Vincent Adam; Maneesh Sahani; Merav Ahissar (CogSci, 2017)

A Universal Marginalizer for Amortized Inference in Generative Models. - Laura Douglas; Iliyan Zarov; Konstantinos Gourgoulias; Chris Lucas 002; Chris Hart; Adam Baker; Maneesh Sahani; Yura Perov; Saurabh Johri (CoRR, 2017)

Temporal alignment and latent Gaussian process factor inference in population spike trains. - Lea Duncker; Maneesh Sahani (NeurIPS, 2018)

Flexible and accurate inference and learning for deep generative models. - Eszter Vértes; Maneesh Sahani (NeurIPS, 2018)

Empirical fixed point bifurcation analysis. - Gergo Bohner; Maneesh Sahani (CoRR, 2018)

Learning interpretable continuous-time models of latent stochastic dynamical systems. - Lea Duncker; Gergo Bohner; Julien Boussard; Maneesh Sahani (ICML, 2019)

Kernel Instrumental Variable Regression. - Rahul Singh; Maneesh Sahani; Arthur Gretton (NeurIPS, 2019)

A neurally plausible model learns successor representations in partially observable environments. - Eszter Vértes; Maneesh Sahani (NeurIPS, 2019)

A neurally plausible model for online recognition and postdiction in a dynamical environment. - Li Kevin Wenliang; Maneesh Sahani (NeurIPS, 2019)

Amortised Learning by Wake-Sleep. - Li K. Wenliang; Theodore H. Moskovitz; Heishiro Kanagawa; Maneesh Sahani (ICML, 2020)

Organizing recurrent network dynamics by task-computation to enable continual learning. - Lea Duncker; Laura Driscoll; Krishna V. Shenoy; Maneesh Sahani; David Sussillo (NeurIPS, 2020)

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data. - Virginia Rutten; Alberto Bernacchia; Maneesh Sahani; Guillaume Hennequin (NeurIPS, 2020)

Probabilistic Tensor Decomposition of Neural Population Spiking Activity. - Hugo Soulat; Sepiedeh Keshavarzi; Troy W. Margrie; Maneesh Sahani (NeurIPS, 2021)

Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning. - Grace W. Lindsay; Josh Merel; Tom Mrsic-Flogel; Maneesh Sahani (CoRR, 2021)

A First-Occupancy Representation for Reinforcement Learning. - Ted Moskovitz; Spencer R. Wilson; Maneesh Sahani (ICLR, 2022)

Learning neural codes for perceptual uncertainty. - Mehrdad Salmasi; Maneesh Sahani (ISIT, 2022)

Structured Recognition for Generative Models with Explaining Away. - Changmin Yu; Hugo Soulat; Neil Burgess; Maneesh Sahani (NeurIPS, 2022)

Amortised Inference in Structured Generative Models with Explaining Away. - Changmin Yu; Hugo Soulat; Neil Burgess; Maneesh Sahani (CoRR, 2022)

Unsupervised representational learning with recognition-parametrised probabilistic models. - William I. Walker; Hugo Soulat; Changmin Yu; Maneesh Sahani (CoRR, 2022)

Deep learning, reinforcement learning, and world models. - Yutaka Matsuo; Yann LeCun; Maneesh Sahani; Doina Precup; David Silver; Masashi Sugiyama; Eiji Uchibe; Jun Morimoto (Neural Networks, 2022)

Unsupervised representation learning with recognition-parametrised probabilistic models. - William I. Walker; Hugo Soulat; Changmin Yu; Maneesh Sahani (AISTATS, 2023)

Minimum Description Length Control. - Ted Moskovitz; Ta-Chu Kao; Maneesh Sahani; Matt M. Botvinick (ICLR, 2023)

A State Representation for Diminishing Rewards. - Ted Moskovitz; Samo Hromadka; Ahmed Touati; Diana Borsa; Maneesh Sahani (NeurIPS, 2023)

Successor-Predecessor Intrinsic Exploration. - Changmin Yu; Neil Burgess; Maneesh Sahani; Samuel J. Gershman (NeurIPS, 2023)

Prediction under Latent Subgroup Shifts with High-Dimensional Observations. - William I. Walker; Arthur Gretton; Maneesh Sahani (CoRR, 2023)

Non-Stationary Learning of Neural Networks with Automatic Soft Parameter Reset. - Alexandre Galashov; Michalis K. Titsias; András György 001; Clare Lyle; Razvan Pascanu; Yee Whye Teh; Maneesh Sahani (NeurIPS, 2024)

Understanding dual process cognition via the minimum description length principle. - Ted Moskovitz; Kevin J. Miller; Maneesh Sahani; Matthew M. Botvinick (PLoS Comput. Biol., 2024)

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA. - Changmin Yu; Maneesh Sahani; Máté Lengyel (ICLR, 2025)

Maximum Likelihood Learning of Latent Dynamics Without Reconstruction. - Samo Hromadka; Kai Biegun; Lior Fox; James Heald; Maneesh Sahani (CoRR, 2025)