Machine Learning meets Neuroscience: from Spikes to Stimulation


Fabian Sinz | CIDAS University of Göttingen, Germany
Alexander Ecker | CIDAS University of Göttingen, Germany


Machine Learning, in particular deep learning, has become an important tool in computational neuroscience to bridge the gap between models and data. Recent experimental techniques in neuroscience yield an increasing amount of rich data that can be fitted with complex models using novel machine learning techniques. This provides a great opportunity to strengthen the link between complex theoretical models and data. For instance, not only have deep networks set new standards in predicting responses of neural populations to arbitrary stimuli and the synthesis of novel stimuli for experimental manipulation, but novel probabilistic machine learning techniques also help to fit parameters of simulation-based (spiking) models to data. At the same time, deep learning serves as an inspiration for how biological networks could learn complex problems using biologically plausible learning rules. With this workshop, we want to highlight the opportunities of modern machine learning in (computational) neuroscience by showcasing a diverse set of successful applications and discussing opportunities for how novel techniques could help test theoretical models with experimental data.

Schedule (CEST)


Janne Lappalainen | University of Tübingen, Germany
Connectome and Task Constrained Neural Networks


Stefano Panzeri | Medical Center Hamburg-Eppendorf (UKE), Germany
Design optogenetic patterns to elicit virtual sensations and control/understand behavior


15 min break


Bernd Illing | EPFL, Lausanne, Switzerland
Biologically plausible deep learning


Viola Priesemann | Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
Tuning task performance via homeostatic plasticity on a neuromorphic chip


Roxana Zeraati | University of Tübingen, Germany
Oleg Vinogradov | University of Tübingen, Germany
Inferring models of neuronal dynamics from data with approximate Bayesian computation


15 min break


Kate Storrs | Justus Liebig University Giessen, Germany
Unsupervised learning for mid-level visual understanding


Andrew Saxe | University of Oxford, UK
Rich and lazy learning of task representations in brains and neural networks