Search Results for author:
Found 6 papers, 5 papers with code
A solution for the mean parametrization of the von Mises-Fisher distribution
The von Mises-Fisher distribution as an exponential family can be expressed in terms of either its natural or its mean parameters.
Learning Implicit PDE Integration with Linear Implicit Layers
As a challenging test case, we train emulators on semi-implicit integration of 2D shallow-water equations with closed boundaries.
Automatic Posterior Transformation for Likelihood-Free Inference
How can one perform Bayesian inference on stochastic simulators with intractable likelihoods?
Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations
Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements.
Flexible statistical inference for mechanistic models of neural dynamics
Our approach builds on recent advances in ABC by learning a neural network which maps features of the observed data to the posterior distribution over parameters.
Signatures of criticality arise in simple neural population models with correlations
Support for this notion has come from a series of studies which identified statistical signatures of criticality in the ensemble activity of retinal ganglion cells.
Neurons and Cognition
