no code implementations • 14 Apr 2022 • Ari Blau, Christoph Gebhardt, Andres Bendesky, Liam Paninski, Anqi Wu

Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology.

no code implementations • NeurIPS 2021 • Julien Boussard, Erdem Varol, Hyun Dong Lee, Nishchal Dethe, Liam Paninski

Neuropixels (NP) probes are dense linear multi-electrode arrays that have rapidly become essential tools for studying the electrophysiology of large neural populations.

1 code implementation • NeurIPS 2020 • Anqi Wu, E. Kelly Buchanan, Matthew Whiteway, Michael Schartner, Guido Meijer, Jean-Paul Noel, Erica Rodriguez, Claire Everett, Amy Norovich, Evan Schaffer, Neeli Mishra, C. Daniel Salzman, Dora Angelaki, Andrés Bendesky, The International Brain Laboratory The International Brain Laboratory, John P. Cunningham, Liam Paninski

Noninvasive behavioral tracking of animals is crucial for many scientific investigations.

no code implementations • NeurIPS 2020 • Joshua Glaser, Matthew Whiteway, John P. Cunningham, Liam Paninski, Scott Linderman

We allow the nature of these interactions to change over time by using a discrete set of dynamical states.

2 code implementations • 29 Oct 2020 • Yueqi Wang, Yoonho Lee, Pallab Basu, Juho Lee, Yee Whye Teh, Liam Paninski, Ari Pakman

While graph neural networks (GNNs) have been successful in encoding graph structures, existing GNN-based methods for community detection are limited by requiring knowledge of the number of communities in advance, in addition to lacking a proper probabilistic formulation to handle uncertainty.

1 code implementation • 5 Jun 2020 • Xue-Xin Wei, Ding Zhou, Andres Grosmark, Zaki Ajabi, Fraser Sparks, Pengcheng Zhou, Mark Brandon, Attila Losonczy, Liam Paninski

However, statistical modeling of deconvolved calcium signals (i. e., the estimated activity extracted by a pre-processing pipeline) is just as critical for interpreting calcium measurements, and for incorporating these observations into downstream probabilistic encoding and decoding models.

1 code implementation • 6 Apr 2020 • Ding Zhou, Yuanjun Gao, Liam Paninski

The Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) has been used widely as a natural Bayesian nonparametric extension of the classical Hidden Markov Model for learning from sequential and time-series data.

no code implementations • 11 Mar 2020 • Jackson Loper, David Blei, John P. Cunningham, Liam Paninski

Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues.

no code implementations • NeurIPS 2019 • Ruoxi Sun, Ian Kinsella, Scott Linderman, Liam Paninski

However, current sensors and imaging approaches still face significant limitations in SNR and sampling frequency; therefore statistical denoising and interpolation methods remain critical for understanding single-trial spatiotemporal dendritic voltage dynamics.

1 code implementation • NeurIPS 2019 • Eleanor Batty, Matthew Whiteway, Shreya Saxena, Dan Biderman, Taiga Abe, Simon Musall, Winthrop Gillis, Jeffrey Markowitz, Anne Churchland, John P. Cunningham, Sandeep R. Datta, Scott Linderman, Liam Paninski

Here we introduce a probabilistic framework for the analysis of behavioral video and neural activity.

1 code implementation • NeurIPS 2019 • Nishal Shah, Sasidhar Madugula, Pawel Hottowy, Alexander Sher, Alan Litke, Liam Paninski, E.J. Chichilnisky

Large-scale, high-density electrical recording and stimulation in primate retina were used as a lab prototype for an artificial retina.

no code implementations • pproximateinference AABI Symposium 2019 • Gonzalo Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski

This problem is known to quickly become intractable as the size of the permutation increases, since its involves the computation of the permanent of a matrix, a #P-hard problem.

no code implementations • pproximateinference AABI Symposium 2019 • Ari Pakman, Yueqi Wang, Liam Paninski

We introduce a neural architecture to perform amortized approximate Bayesian inference over latent random permutations of two sets of objects.

1 code implementation • NeurIPS Workshop Neuro_AI 2019 • Yueqi Wang, Ari Pakman, Catalin Mitelut, JinHyung Lee, Liam Paninski

We present a novel approach to spike sorting for high-density multielectrode probes using the Neural Clustering Process (NCP), a recently introduced neural architecture that performs scalable amortized approximate Bayesian inference for efficient probabilistic clustering.

5 code implementations • ICML 2020 • Ari Pakman, Yueqi Wang, Catalin Mitelut, JinHyung Lee, Liam Paninski

Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces.

1 code implementation • 24 Nov 2018 • Ari Pakman, Liam Paninski

We develop methods for efficient amortized approximate Bayesian inference over posterior distributions of probabilistic clustering models, such as Dirichlet process mixture models.

no code implementations • 6 Nov 2018 • Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski

We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear, smooth latent dynamics from sequential data.

no code implementations • 27 Sep 2018 • Daniel Hernandez Diaz, Antonio Khalil Moretti, Ziqiang Wei, Shreya Saxena, John Cunningham, Liam Paninski

In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear.

1 code implementation • 17 Jul 2018 • E. Kelly Buchanan, Ian Kinsella, Ding Zhou, Rong Zhu, Pengcheng Zhou, Felipe Gerhard, John Ferrante, Ying Ma, Sharon Kim, Mohammed Shaik, Yajie Liang, Rongwen Lu, Jacob Reimer, Paul Fahey, Taliah Muhammad, Graham Dempsey, Elizabeth Hillman, Na Ji, Andreas Tolias, Liam Paninski

Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution.

1 code implementation • ICML 2018 • Ruoxi Sun, Liam Paninski

This approach is therefore highly flexible and improves on the state of the art in terms of accuracy; provides uncertainty estimates about the particle locations and identities; and has a test run-time that scales linearly as a function of the data length and number of particles, thus enabling Bayesian inference in arbitrarily large particle tracking datasets.

1 code implementation • NeurIPS 2017 • Jin Hyung Lee, David E. Carlson, Hooshmand Shokri Razaghi, Weichi Yao, Georges A. Goetz, Espen Hagen, Eleanor Batty, E.J. Chichilnisky, Gaute T. Einevoll, Liam Paninski

Spike sorting is a critical first step in extracting neural signals from large-scale electrophysiological data.

1 code implementation • 1 Dec 2017 • E. Kelly Buchanan, Akiva Lipshitz, Scott Linderman, Liam Paninski

In order to fully understand the neural activity of Caenorhabditis elegans, we need a rich, quantitative description of the behavioral outputs it gives rise to.

1 code implementation • NeurIPS 2017 • Nikhil Parthasarathy, Eleanor Batty, William Falcon, Thomas Rutten, Mohit Rajpal, E.J. Chichilnisky, Liam Paninski

Decoding sensory stimuli from neural signals can be used to reveal how we sense our physical environment, and is valuable for the design of brain-machine interfaces.

no code implementations • NeurIPS 2017 • Andrea Giovannucci, Johannes Friedrich, Matt Kaufman, Anne Churchland, Dmitri Chklovskii, Liam Paninski, Eftychios A. Pnevmatikakis

Optical imaging methods using calcium indicators are critical for monitoring the activity of large neuronal populations in vivo.

no code implementations • 26 Oct 2017 • Scott W. Linderman, Gonzalo E. Mena, Hal Cooper, Liam Paninski, John P. Cunningham

Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension.

no code implementations • NeurIPS 2016 • Johannes Friedrich, Liam Paninski

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations.

1 code implementation • 26 Oct 2016 • Scott W. Linderman, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski, Matthew J. Johnson

Many natural systems, such as neurons firing in the brain or basketball teams traversing a court, give rise to time series data with complex, nonlinear dynamics.

1 code implementation • ICML 2017 • Ari Pakman, Dar Gilboa, David Carlson, Liam Paninski

We introduce a novel stochastic version of the non-reversible, rejection-free Bouncy Particle Sampler (BPS), a Markov process whose sample trajectories are piecewise linear.

no code implementations • 24 Jun 2016 • Kamiar Rahnama Rad, Timothy A. Machado, Liam Paninski

On the other hand, sharing information between adjacent neurons can errantly degrade estimates of tuning functions across space if there are sharp discontinuities in tuning between nearby neurons.

no code implementations • NeurIPS 2016 • Yuanjun Gao, Evan Archer, Liam Paninski, John P. Cunningham

A body of recent work in modeling neural activity focuses on recovering low-dimensional latent features that capture the statistical structure of large-scale neural populations.

8 code implementations • 24 May 2016 • Pengcheng Zhou, Shanna L. Resendez, Jose Rodriguez-Romaguera, Jessica C. Jimenez, Shay Q. Neufeld, Garret D. Stuber, Rene Hen, Mazen A. Kheirbek, Bernardo L. Sabatini, Robert E. Kass, Liam Paninski

In vivo calcium imaging through microscopes has enabled deep brain imaging of previously inaccessible neuronal populations within the brains of freely moving subjects.

no code implementations • 7 Mar 2016 • David Carlson, Patrick Stinson, Ari Pakman, Liam Paninski

Partition functions of probability distributions are important quantities for model evaluation and comparisons.

no code implementations • 23 Nov 2015 • Evan Archer, Il Memming Park, Lars Buesing, John Cunningham, Liam Paninski

These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time.

no code implementations • 13 Nov 2015 • Josh Merel, David Carlson, Liam Paninski, John P. Cunningham

We describe how training a decoder in this way is a novel variant of an imitation learning problem, where an oracle or expert is employed for supervised training in lieu of direct observations, which are not available.

no code implementations • NeurIPS 2014 • Lars Buesing, Timothy A. Machado, John P. Cunningham, Liam Paninski

High-dimensional, simultaneous recordings of neural spiking activity are often explored, analyzed and visualized with the help of latent variable or factor models.

11 code implementations • 9 Sep 2014 • Eftychios A. Pnevmatikakis, Yuanjun Gao, Daniel Soudry, David Pfau, Clay Lacefield, Kira Poskanzer, Randy Bruno, Rafael Yuste, Liam Paninski

We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles.

Neurons and Cognition Quantitative Methods Applications

no code implementations • NeurIPS 2013 • Ben Shababo, Brooks Paige, Ari Pakman, Liam Paninski

We develop an inference and optimal design procedure for recovering synaptic weights in neural microcircuits.

no code implementations • NeurIPS 2013 • David Pfau, Eftychios A. Pnevmatikakis, Liam Paninski

We show on model data that the parameters of latent linear dynamical systems can be recovered, and that even if the dynamics are not stationary we can still recover the true latent subspace.

no code implementations • NeurIPS 2013 • Eftychios A. Pnevmatikakis, Liam Paninski

We propose a compressed sensing (CS) calcium imaging framework for monitoring large neuronal populations, where we image randomized projections of the spatial calcium concentration at each timestep, instead of measuring the concentration at individual locations.

no code implementations • NeurIPS 2013 • Josh S. Merel, Roy Fox, Tony Jebara, Liam Paninski

In a closed-loop brain-computer interface (BCI), adaptive decoders are used to learn parameters suited to decoding the user's neural response.

5 code implementations • 27 Nov 2013 • Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman, Liam Paninski

We present efficient Bayesian methods for extracting neuronal spiking information from calcium imaging data.

Neurons and Cognition Quantitative Methods Applications

1 code implementation • 20 Aug 2012 • Ari Pakman, Liam Paninski

We present a Hamiltonian Monte Carlo algorithm to sample from multivariate Gaussian distributions in which the target space is constrained by linear and quadratic inequalities or products thereof.

Computation Applications

no code implementations • NeurIPS 2011 • Kamiar R. Rad, Liam Paninski

Many fundamental questions in theoretical neuroscience involve optimal decoding and the computation of Shannon information rates in populations of spiking neurons.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.