no code implementations • 5 Jul 2021 • Juliane Weilbach, Sebastian Gerwinn, Christian Weilbach, Melih Kandemir
Understanding physical phenomena oftentimes means understanding the underlying dynamical system that governs observational measurements.
no code implementations • 17 Jun 2020 • Manuel Haussmann, Sebastian Gerwinn, Andreas Look, Barbara Rakitsch, Melih Kandemir
Neural Stochastic Differential Equations model a dynamical environment with neural nets assigned to their drift and diffusion terms.
1 code implementation • pproximateinference AABI Symposium 2021 • Manuel Haussmann, Sebastian Gerwinn, Melih Kandemir
We propose a novel method for closed-form predictive distribution modeling with neural nets.
1 code implementation • NeurIPS 2018 • David Reeb, Andreas Doerr, Sebastian Gerwinn, Barbara Rakitsch
Gaussian Processes (GPs) are a generic modelling tool for supervised learning.
no code implementations • NeurIPS 2009 • Sebastian Gerwinn, Leonard White, Matthias Kaschube, Matthias Bethge, Jakob H. Macke
Imaging techniques such as optical imaging of intrinsic signals, 2-photon calcium imaging and voltage sensitive dye imaging can be used to measure the functional organization of visual cortex across different spatial scales.
no code implementations • NeurIPS 2009 • Sebastian Gerwinn, Philipp Berens, Matthias Bethge
Second-order maximum-entropy models have recently gained much interest for describing the statistics of binary spike trains.
no code implementations • NeurIPS 2009 • Philipp Berens, Sebastian Gerwinn, Alexander Ecker, Matthias Bethge
In this way, we provide a new rigorous framework for assessing the functional consequences of noise correlation structures for the representational accuracy of neural population codes that is in particular applicable to short-time population coding.