647 citations · 1.7k across the 29 of their papers we have counts for
30 papers
SemiMultiPose: A Semi-supervised Multi-animal Pose Estimation Framework
Ari Blau, Christoph Gebhardt, Andres Bendesky +2
Multi-animal pose estimation is essential for studying animals' social behaviors in neuroscience and neuroethology. Advanced approaches have been proposed to support multi-animal e…
Chronic, cortex-wide imaging of specific cell populations during behavior
Joao Couto, Simon Musall, Xiaonan R Sun +8
Measurements of neuronal activity across brain areas are important for understanding the neural correlates of cognitive and motor processes like attention, decision-making, and act…
Amortized Probabilistic Detection of Communities in Graphs
Yueqi Wang, Yoonho Lee, Pallab Basu +4
Learning community structures in graphs has broad applications across scientific domains. While graph neural networks (GNNs) have been successful in encoding graph structures, exis…
A zero-inflated gamma model for deconvolved calcium imaging traces
Xue-Xin Wei, Ding Zhou, Andres Grosmark +6
Calcium imaging is a critical tool for measuring the activity of large neural populations. Much effort has been devoted to developing "pre-processing" tools for calcium video data,…
Disentangled Sticky Hierarchical Dirichlet Process Hidden Markov Model
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 learnin…
Linear-time inference for Gaussian Processes on one dimension
Jackson Loper, David Blei, John P. Cunningham +1
Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues. Here we in…
Neural Clustering Processes
Ari Pakman, Yueqi Wang, Catalin Mitelut +2
Probabilistic clustering models (or equivalently, mixture models) are basic building blocks in countless statistical models and involve latent random variables over discrete spaces…
Amortized Bayesian inference for clustering models
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.…
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
Daniel Hernandez, Antonio Khalil Moretti, Ziqiang Wei +3
Latent variable models have been widely applied for the analysis of time series resulting from experimental neuroscience techniques. In these datasets, observations are relatively…
Penalized matrix decomposition for denoising, compression, and improved demixing of functional imaging data
E. Kelly Buchanan, Ian Kinsella, Ding Zhou +17
Calcium imaging has revolutionized systems neuroscience, providing the ability to image large neural populations with single-cell resolution. The resulting datasets are quite large…
Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Scott W. Linderman, Gonzalo E. Mena, Hal Cooper +2
Many matching, tracking, sorting, and ranking problems require probabilistic reasoning about possible permutations, a set that grows factorially with dimension. Combinatorial optim…
Automated scalable segmentation of neurons from multispectral images
Uygar Sümbül, Douglas Roussien, Fei Chen +5
Reconstruction of neuroanatomy is a fundamental problem in neuroscience. Stochastic expression of colors in individual cells is a promising tool, although its use in the nervous sy…
Recurrent switching linear dynamical systems
Scott W. Linderman, Andrew C. Miller, Ryan P. Adams +3
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. We can gain insigh…
Stochastic Bouncy Particle Sampler
Ari Pakman, Dar Gilboa, David Carlson +1
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. The al…
Fast Online Deconvolution of Calcium Imaging Data
Johannes Friedrich, Pengcheng Zhou, Liam Paninski
Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescenc…
Robust and scalable Bayesian analysis of spatial neural tuning function data
Kamiar Rahnama Rad, Timothy A. Machado, Liam Paninski
A common analytical problem in neuroscience is the interpretation of neural activity with respect to sensory input or behavioral output. This is typically achieved by regressing me…
Linear dynamical neural population models through nonlinear embeddings
Yuanjun Gao, Evan Archer, Liam Paninski +1
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. Mo…
Bayesian methods for event analysis of intracellular currents
Josh Merel, Ben Shababo, Alex Naka +2
Investigation of neural circuit functioning often requires statistical interpretation of events in subthreshold electrophysiological recordings. This problem is non-trivial because…
Partition Functions from Rao-Blackwellized Tempered Sampling
David Carlson, Patrick Stinson, Ari Pakman +1
Partition functions of probability distributions are important quantities for model evaluation and comparisons. We present a new method to compute partition functions of complex an…
Black box variational inference for state space models
Evan Archer, Il Memming Park, Lars Buesing +2
Latent variable time-series models are among the most heavily used tools from machine learning and applied statistics. These models have the advantage of learning latent structure…
Neuroprosthetic decoder training as imitation learning
Josh Merel, David Carlson, Liam Paninski +1
Neuroprosthetic brain-computer interfaces function via an algorithm which decodes neural activity of the user into movements of an end effector, such as a cursor or robotic arm. In…
A structured matrix factorization framework for large scale calcium imaging data analysis
Eftychios A. Pnevmatikakis, Yuanjun Gao, Daniel Soudry +6
We present a structured matrix factorization approach to analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of t…
Bayesian spike inference from calcium imaging data
Eftychios A. Pnevmatikakis, Josh Merel, Ari Pakman +1
We present efficient Bayesian methods for extracting neuronal spiking information from calcium imaging data. The goal of our methods is to sample from the posterior distribution of…
Auxiliary-variable Exact Hamiltonian Monte Carlo Samplers for Binary Distributions
Ari Pakman, Liam Paninski
We present a new approach to sample from generic binary distributions, based on an exact Hamiltonian Monte Carlo algorithm applied to a piecewise continuous augmentation of the bin…