Timezone: »
Neural circuits contain heterogeneous groups of neurons that differ in type, location, connectivity, and basic response properties. However, traditional methods for dimensionality reduction and clustering are ill-suited to recovering the structure underlying the organization of neural circuits. In particular, they do not take advantage of the rich temporal dependencies in multi-neuron recordings and fail to account for the noise in neural spike trains. Here we describe new tools for inferring latent structure from simultaneously recorded spike train data using a hierarchical extension of a multi-neuron point process model commonly known as the generalized linear model (GLM). Our approach combines the GLM with flexible graph-theoretic priors governing the relationship between latent features and neural connectivity patterns. Fully Bayesian inference via Pólya-gamma augmentation of the resulting model allows us to classify neurons and infer latent dimensions of circuit organization from correlated spike trains. We demonstrate the effectiveness of our method with applications to synthetic data and multi-neuron recordings in primate retina, revealing latent patterns of neural types and locations from spike trains alone.
Author Information
Scott Linderman (Columbia University)
Ryan Adams (Princeton University)
Jonathan Pillow (Princeton University)
More from the Same Authors
-
2021 Spotlight: Slice Sampling Reparameterization Gradients »
David Zoltowski · Diana Cai · Ryan Adams -
2021 Spotlight: Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate »
Xingyuan Sun · Tianju Xue · Szymon Rusinkiewicz · Ryan Adams -
2021 : Neural Latents Benchmark ‘21: Evaluating latent variable models of neural population activity »
Felix Pei · Joel Ye · David Zoltowski · Anqi Wu · Raeed Chowdhury · Hansem Sohn · Joseph O'Doherty · Krishna V Shenoy · Matthew Kaufman · Mark Churchland · Mehrdad Jazayeri · Lee Miller · Jonathan Pillow · Il Memming Park · Eva Dyer · Chethan Pandarinath -
2021 : ProBF: Probabilistic Safety Certificates with Barrier Functions »
Sulin Liu · Athindran Ramesh Kumar · Jaime Fisac · Ryan Adams · Peter J. Ramadge -
2021 : Reading the Road: Leveraging Meta-Learning to Learn Other Driver Behavior »
Anat Kleiman · Ryan Adams -
2022 : Non-exchangeability in Infinite Switching Linear Dynamical Systems »
Victor Geadah · Jonathan Pillow -
2022 : A code superoptimizer through neural Monte-Carlo tree search »
Wenda Zhou · Olga Solodova · Ryan Adams -
2022 : A code superoptimizer through neural Monte-Carlo tree search »
Wenda Zhou · Olga Solodova · Ryan Adams -
2022 Poster: Multi-fidelity Monte Carlo: a pseudo-marginal approach »
Diana Cai · Ryan Adams -
2022 Poster: Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior »
Zoe Ashwood · Aditi Jha · Jonathan Pillow -
2022 Poster: Extracting computational mechanisms from neural data using low-rank RNNs »
Adrian Valente · Jonathan Pillow · Srdjan Ostojic -
2021 : Randomized Automatic Differentiation - Ryan Adams - Princeton University »
Ryan Adams -
2021 Poster: Slice Sampling Reparameterization Gradients »
David Zoltowski · Diana Cai · Ryan Adams -
2021 Poster: Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate »
Xingyuan Sun · Tianju Xue · Szymon Rusinkiewicz · Ryan Adams -
2021 Poster: Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability »
Dibya Ghosh · Jad Rahme · Aviral Kumar · Amy Zhang · Ryan Adams · Sergey Levine -
2020 : Orals 1.1: Randomized Automatic Differentiation »
Deniz Oktay · Nick McGreivy · Alex Beatson · Ryan Adams -
2020 Workshop: Machine Learning for Engineering Modeling, Simulation and Design »
Alex Beatson · Priya Donti · Amira Abdel-Rahman · Stephan Hoyer · Rose Yu · J. Zico Kolter · Ryan Adams -
2020 Poster: High-contrast “gaudy” images improve the training of deep neural network models of visual cortex »
Benjamin Cowley · Jonathan Pillow -
2020 Poster: On Warm-Starting Neural Network Training »
Jordan Ash · Ryan Adams -
2020 Poster: Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters »
Sulin Liu · Xingyuan Sun · Peter J. Ramadge · Ryan Adams -
2020 Poster: Identifying signal and noise structure in neural population activity with Gaussian process factor models »
Stephen Keeley · Mikio Aoi · Yiyi Yu · Spencer Smith · Jonathan Pillow -
2020 Poster: Inferring learning rules from animal decision-making »
Zoe Ashwood · Nicholas Roy · Ji Hyun Bak · Jonathan Pillow -
2020 Poster: Learning Composable Energy Surrogates for PDE Order Reduction »
Alex Beatson · Jordan Ash · Geoffrey Roeder · Tianju Xue · Ryan Adams -
2020 Oral: Learning Composable Energy Surrogates for PDE Order Reduction »
Alex Beatson · Jordan Ash · Geoffrey Roeder · Tianju Xue · Ryan Adams -
2019 Poster: SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers »
Igor Fedorov · Ryan Adams · Matthew Mattina · Paul Whatmough -
2019 Poster: Discrete Object Generation with Reversible Inductive Construction »
Ari Seff · Wenda Zhou · Farhan Damani · Abigail Doyle · Ryan Adams -
2018 : Discussion Panel: Ryan Adams, Nicolas Heess, Leslie Kaelbling, Shie Mannor, Emo Todorov (moderator: Roy Fox) »
Ryan Adams · Nicolas Heess · Leslie Kaelbling · Shie Mannor · Emo Todorov · Roy Fox -
2018 : Inference and Control of Learning Behavior in Rodents (Ryan Adams) »
Ryan Adams -
2018 Poster: Scaling the Poisson GLM to massive neural datasets through polynomial approximations »
David Zoltowski · Jonathan Pillow -
2018 Poster: Efficient inference for time-varying behavior during learning »
Nicholas Roy · Ji Hyun Bak · Athena Akrami · Carlos Brody · Jonathan Pillow -
2018 Poster: A Bayesian Nonparametric View on Count-Min Sketch »
Diana Cai · Michael Mitzenmacher · Ryan Adams -
2018 Poster: Model-based targeted dimensionality reduction for neuronal population data »
Mikio Aoi · Jonathan Pillow -
2018 Poster: Power-law efficient neural codes provide general link between perceptual bias and discriminability »
Michael J Morais · Jonathan Pillow -
2018 Poster: Learning a latent manifold of odor representations from neural responses in piriform cortex »
Anqi Wu · Stan Pashkovski · Sandeep Datta · Jonathan Pillow -
2017 Poster: PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
Jonathan Huggins · Ryan Adams · Tamara Broderick -
2017 Spotlight: PASS-GLM: polynomial approximate sufficient statistics for scalable Bayesian GLM inference »
Jonathan Huggins · Ryan Adams · Tamara Broderick -
2017 Poster: Reducing Reparameterization Gradient Variance »
Andrew Miller · Nick Foti · Alexander D'Amour · Ryan Adams -
2017 Poster: Gaussian process based nonlinear latent structure discovery in multivariate spike train data »
Anqi Wu · Nicholas Roy · Stephen Keeley · Jonathan Pillow -
2016 : Panel Discussion »
Shakir Mohamed · David Blei · Ryan Adams · José Miguel Hernández-Lobato · Ian Goodfellow · Yarin Gal -
2016 : A Tribute to David MacKay »
Ryan Adams -
2016 Workshop: Bayesian Optimization: Black-box Optimization and Beyond »
Roberto Calandra · Bobak Shahriari · Javier Gonzalez · Frank Hutter · Ryan Adams -
2016 : Leveraging Structure in Bayesian Optimization »
Ryan Adams -
2016 Poster: Adaptive optimal training of animal behavior »
Ji Hyun Bak · Jung Choi · Ilana Witten · Athena Akrami · Jonathan Pillow -
2016 Poster: A Bayesian method for reducing bias in neural representational similarity analysis »
Mingbo Cai · Nicolas W Schuck · Jonathan Pillow · Yael Niv -
2016 Poster: Composing graphical models with neural networks for structured representations and fast inference »
Matthew Johnson · David Duvenaud · Alex Wiltschko · Ryan Adams · Sandeep R Datta -
2015 Workshop: Bayesian Optimization: Scalability and Flexibility »
Bobak Shahriari · Ryan Adams · Nando de Freitas · Amar Shah · Roberto Calandra -
2015 Workshop: Statistical Methods for Understanding Neural Systems »
Alyson Fletcher · Jakob H Macke · Ryan Adams · Jascha Sohl-Dickstein -
2015 Poster: Convolutional spike-triggered covariance analysis for neural subunit models »
Anqi Wu · Il Memming Park · Jonathan Pillow -
2015 Poster: Convolutional Networks on Graphs for Learning Molecular Fingerprints »
David Duvenaud · Dougal Maclaurin · Jorge Iparraguirre · Rafael Bombarell · Timothy Hirzel · Alan Aspuru-Guzik · Ryan Adams -
2015 Poster: A Gaussian Process Model of Quasar Spectral Energy Distributions »
Andrew Miller · Albert Wu · Jeffrey Regier · Jon McAuliffe · Dustin Lang · Mr. Prabhat · David Schlegel · Ryan Adams -
2015 Poster: Spectral Representations for Convolutional Neural Networks »
Oren Rippel · Jasper Snoek · Ryan Adams -
2015 Poster: Dependent Multinomial Models Made Easy: Stick-Breaking with the Polya-gamma Augmentation »
Scott Linderman · Matthew Johnson · Ryan Adams -
2014 Workshop: Bayesian Optimization in Academia and Industry »
Zoubin Ghahramani · Ryan Adams · Matthew Hoffman · Kevin Swersky · Jasper Snoek -
2014 Poster: A framework for studying synaptic plasticity with neural spike train data »
Scott Linderman · Christopher H Stock · Ryan Adams -
2013 Workshop: Bayesian Optimization in Theory and Practice »
Matthew Hoffman · Jasper Snoek · Nando de Freitas · Michael A Osborne · Ryan Adams · Sebastien Bubeck · Philipp Hennig · Remi Munos · Andreas Krause -
2013 Poster: Multi-Task Bayesian Optimization »
Kevin Swersky · Jasper Snoek · Ryan Adams -
2013 Poster: Message Passing Inference with Chemical Reaction Networks »
Nils E Napp · Ryan Adams -
2013 Oral: Message Passing Inference with Chemical Reaction Networks »
Nils E Napp · Ryan Adams -
2013 Poster: A Determinantal Point Process Latent Variable Model for Inhibition in Neural Spiking Data »
Jasper Snoek · Richard Zemel · Ryan Adams -
2013 Poster: Contrastive Learning Using Spectral Methods »
James Y Zou · Daniel Hsu · David Parkes · Ryan Adams -
2012 Poster: Bayesian n-Choose-k Models for Classification and Ranking »
Kevin Swersky · Danny Tarlow · Richard Zemel · Ryan Adams · Brendan J Frey -
2012 Poster: Priors for Diversity in Generative Latent Variable Models »
James Y Zou · Ryan Adams -
2012 Poster: Cardinality Restricted Boltzmann Machines »
Kevin Swersky · Danny Tarlow · Ilya Sutskever · Richard Zemel · Russ Salakhutdinov · Ryan Adams -
2012 Poster: Practical Bayesian Optimization of Machine Learning Algorithms »
Jasper Snoek · Hugo Larochelle · Ryan Adams -
2011 Workshop: Bayesian Nonparametric Methods: Hope or Hype? »
Emily Fox · Ryan Adams -
2010 Workshop: Transfer Learning Via Rich Generative Models. »
Russ Salakhutdinov · Ryan Adams · Josh Tenenbaum · Zoubin Ghahramani · Tom Griffiths -
2010 Workshop: Monte Carlo Methods for Bayesian Inference in Modern Day Applications »
Ryan Adams · Mark A Girolami · Iain Murray -
2010 Oral: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Oral: Slice sampling covariance hyperparameters of latent Gaussian models »
Iain Murray · Ryan Adams -
2010 Poster: Tree-Structured Stick Breaking for Hierarchical Data »
Ryan Adams · Zoubin Ghahramani · Michael Jordan -
2010 Poster: Slice sampling covariance hyperparameters of latent Gaussian models »
Iain Murray · Ryan Adams -
2008 Poster: The Gaussian Process Density Sampler »
Ryan Adams · Iain Murray · David MacKay -
2008 Spotlight: The Gaussian Process Density Sampler »
Ryan Adams · Iain Murray · David MacKay