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Modern datasets in neuroscience enable unprecedented inquiries into the relationship between complex behaviors and the activity of many simultaneously recorded neurons.
Deterministic Nonperiodic Flow
Edward N. Lorenz · 1963
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Spatio-temporal correlations and visual signalling in a complete neuronal population
Jonathan W. Pillow, Jonathon Shlens, Liam Paninski, Alexander Sher, Alan M. Litke, E. J. Chichilnisky, and Eero P. Simoncelli · 2008
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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, and Maneesh Sahani · 2009
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Empirical models of spiking in neural populations
Jakob H Macke, Lars Buesing, John P Cunningham, Byron M Yu, Krishna V Shenoy, and Maneesh Sahani · 2011
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Dynamical segmentation of single trials from population neural data
Biljana Petreska, Byron M Yu, John P Cunningham, Gopal Santhanam, Stephen Ryu, Krishna V Shenoy, and Maneesh Sahani · 2011
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Neural population dynamics during reaching
Mark M. Churchland, John P. Cunningham, Matthew T. Kaufman, Justin D. Foster, Paul Nuyujukian, Stephen I. Ryu, and Krishna V. Shenoy · 2012
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Modeling the impact of common noise inputs on the network activity of retinal ganglion cells
Michael Vidne, Yashar Ahmadian, Jonathon Shlens, Jonathan W. Pillow, Jayant Kulkarni, Alan M. Litke, E. J. Chichilnisky, Eero Simoncelli, and Liam Paninski · 2012
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Whole-brain functional imaging at cellular resolution using light-sheet microscopy
Misha B. Ahrens, Michael B. Orger, Drew N. Robson, Jennifer M. Li, and Philipp J. Keller · 2013
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Robust learning of low-dimensional dynamics from large neural ensembles
David Pfau, Eftychios A Pnevmatikakis, and Liam Paninski · 2013
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Low-dimensional models of neural population activity in sensory cortical circuits
Evan W Archer, Urs Koster, Jonathan W Pillow, and Jakob H Macke · 2014
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Dimensionality reduction for large-scale neural recordings
John P Cunningham and Byron M Yu · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
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Stochastic backpropagation and approximate inference in deep generative models
Danilo Jimenez Rezende, Shakir Mohamed, and Daan Wierstra · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli · 2015
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A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging
Nicholas James Sofroniew, Daniel Flickinger, Jonathan King, and Karel Svoboda · 2016
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Lfads - latent factor analysis via dynamical systems, 2016
David Sussillo, Rafal Jozefowicz, L. F. Abbott, and Chethan Pandarinath · 2016
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Fully integrated silicon probes for high-density recording of neural activity
James J. Jun, Nicholas A. Steinmetz, Joshua H. Siegle, et al · 2017
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Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems
Scott Linderman, Matthew Johnson, Andrew Miller, Ryan Adams, David Blei, and Liam Paninski · 2017
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Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology, 2017
Joseph E. O’Doherty, Mariana M. B. Cardoso, Joseph G. Makin, and Philip N. Sabes · 2017
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Capturing the Dynamical Repertoire of Single Neurons with Generalized Linear Models
Alison I. Weber and Jonathan W. Pillow · 2017
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Gaussian process based nonlinear latent structure discovery in multivariate spike train data
Anqi Wu, Nicholas A Roy, Stephen Keeley, and Jonathan W Pillow · 2017
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Variational latent gaussian process for recovering single-trial dynamics from population spike trains
Yuan Zhao and Il Memming Park · 2017
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Temporal alignment and latent gaussian process factor inference in population spike trains
Lea Duncker and Maneesh Sahani · 2018
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Synthesizing realistic neural population activity patterns using generative adversarial networks
Manuel Molano-Mazon, Arno Onken, Eugenio Piasini, and Stefano Panzeri · 2018
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Mcmaze: macaque primary motor and dorsal premotor cortex spiking activity during delayed reaching (version 0.220113.0400) [data set], 2022
Mark Churchland and Matthew Kaufman · 2022
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It’s raw! audio generation with state-space models
Karan Goel, Albert Gu, Chris Donahue, and Christopher R’e · 2022
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Efficiently modeling long sequences with structured state spaces
Albert Gu, Karan Goel, and Christopher Ré · 2022
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A large-scale neural network training framework for generalized estimation of single-trial population dynamics
Mohammad Reza Keshtkaran, Andrew R. Sedler, Raeed H. Chowdhury, Raghav Tandon, Diya Basrai, Sarah L. Nguyen, Hansem Sohn, Mehrdad Jazayeri, Lee E. Miller, and Chethan Pandarinath · 2022
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Scalable diffusion models with transformers
William S. Peebles and Saining Xie · 2022
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Inferring single-trial neural population dynamics using sequential auto-encoders
Chethan Pandarinath, Daniel J. O’Shea, Jasmine Collins, Rafal Jozefowicz, Sergey D. Stavisky, Jonathan C. Kao, Eric M. Trautmann, Matthew T. Kaufman, Stephen I. Ryu, Leigh R. Hochberg, Jaimie M. Henderson, Krishna V. Shenoy, L. F. Abbott, and David Sussillo · 2018
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Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Mohammad Reza Keshtkaran and Chethan Pandarinath · 2019
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Adversarial training of neural encoding models on population spike trains
Poornima Ramesh, Mohamad Atayi, and Jakob H Macke · 2019
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From variational to deterministic autoencoders
Partha Ghosh, Mehdi S. M. Sajjadi, Antonio Vergari, Michael Black, and Bernhard Scholkopf · 2020
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Denoising Diffusion Probabilistic Models
Jonathan Ho, Ajay Jain, and Pieter Abbeel · 2020
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Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Ding Zhou and Xue-Xin Wei · 2020
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A flow-based latent state generative model of neural population responses to natural images
Mohammad Bashiri, Edgar Walker, Konstantin-Klemens Lurz, Akshay Jagadish, Taliah Muhammad, Zhiwei Ding, Zhuokun Ding, Andreas Tolias, and Fabian Sinz · 2021
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High-resolution image synthesis with latent diffusion models
Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer · 2022
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A Unified, Scalable Framework for Neural Population Decoding
Mehdi Azabou, Vinam Arora, Venkataramana Ganesh, Ximeng Mao, Santosh Nachimuthu, Michael Mendelson, Blake Richards, Matthew Perich, Guillaume Lajoie, and Eva Dyer · 2023
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Seeing beyond the brain: Conditional diffusion model with sparse masked modeling for vision decoding
Zijiao Chen, Jiaxin Qing, Tiange Xiang, Wan Lin Yue, and Juan Helen Zhou · 2023
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Distance function for spike prediction, 2023
Kevin Doran, Marvin Seifert, Carola A. M. Yovanovich, and Tom Baden · 2023
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Generalized teacher forcing for learning chaotic dynamics
Florian Hess, Zahra Monfared, Manuel Brenner, and Daniel Durstewitz · 2023
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Behavioral decomposition reveals rich encoding structure employed across neocortex in rats
Bartul Mimica, Tuçe Tombaz, Claudia Battistin, Jingyi Guo Fuglstad, Benjamin A. Dunn, and Jonathan R. Whitlock · 2023
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lfads-torch: A modular and extensible implementation of latent factor analysis via dynamical systems
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Generating realistic neurophysiological time series with denoising diffusion probabilistic models
Julius Vetter, Jakob H Macke, and Richard Gao · 2023
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Geometric latent diffusion models for 3d molecule generation
Minkai Xu, Alexander Powers, R. Dror, Stefano Ermon, and J. Leskovec · 2023
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Neural Data Transformer 2: Multi-context Pretraining for Neural Spiking Activity
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Neuroformer: Multimodal and Multitask Generative Pretraining for Brain Data
Antonis Antoniades, Yiyi Yu, Joe S. Canzano, William Yang Wang, and Spencer Smith · 2024
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