Bayesian Neural System identification: error bars, receptive fields and neural couplings
Abstract
The task of system identification lies at the heart of neural data analysis. Bayesian system identification methods provide a powerful toolbox which allows one to make inferences over stimulus-neuron and neuron-neuron dependencies in a principled way. Rather than reporting only
Matthias Bethge
Professor for Computational Neuroscience and Machine Learning & Director of the Tübingen AI Center
Matthias Bethge is Professor for Computational Neuroscience and Machine Learning at the University of Tübingen and director of the Tübingen AI Center, a joint center between Tübingen University and MPI for Intelligent Systems that is part of the German AI strategy.