
Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems
Recurrent neural networks (RNNs) are powerful models for processing time...
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Generalized Shape Metrics on Neural Representations
Understanding the operation of biological and artificial networks remain...
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Statistical Neuroscience in the Single Trial Limit
Individual neurons often produce highly variable responses over nominall...
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Fast deep learning correspondence for neuron tracking and identification in C.elegans using synthetic training
We present an automated method to track and identify neurons in C. elega...
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Point process models for sequence detection in highdimensional neural spike trains
Sparse sequences of neural spikes are posited to underlie aspects of wor...
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Unifying and generalizing models of neural dynamics during decisionmaking
An open question in systems and computational neuroscience is how neural...
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PoissonRandomized Gamma Dynamical Systems
This paper presents the Poissonrandomized gamma dynamical system (PRGDS...
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Doseresponse modeling in highthroughput cancer drug screenings: A case study with recommendations for practitioners
Personalized cancer treatments based on the molecular profile of a patie...
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Reparameterizing the Birkhoff Polytope for Variational Permutation Inference
Many matching, tracking, sorting, and ranking problems require probabili...
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Variational Sequential Monte Carlo
Variational inference underlies many recent advances in large scale prob...
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Recurrent switching linear dynamical systems
Many natural systems, such as neurons firing in the brain or basketball ...
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Bayesian latent structure discovery from multineuron recordings
Neural circuits contain heterogeneous groups of neurons that differ in t...
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Reparameterization Gradients through AcceptanceRejection Sampling Algorithms
Variational inference using the reparameterization trick has enabled lar...
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Dependent Multinomial Models Made Easy: Stick Breaking with the PólyaGamma Augmentation
Many practical modeling problems involve discrete data that are best rep...
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A Nonparametric Bayesian Approach to Uncovering Rat Hippocampal Population Codes During Spatial Navigation
Rodent hippocampal population codes represent important spatial informat...
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A framework for studying synaptic plasticity with neural spike train data
Learning and memory in the brain are implemented by complex, timevaryin...
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Discovering Latent Network Structure in Point Process Data
Networks play a central role in modern data analysis, enabling us to rea...
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Scott W. Linderman
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