Workshop Program

Computational models are essential to uncover the functional principles of neuronal processing systems. They are also of increasing importance in understanding the evolutionary origins and adaptations of neuronal processing through comparative and theory-based approaches. Progress in machine learning and data science is currently opening exciting perspectives towards the effective and controlled inference of complex and generative computational models from large-scale and increasingly multimodal data sets. Utilizing, adapting and expanding such techniques to answer fundamental questions in nervous system evolution and comparative neuroscience represents an important challenge at the frontier between computational and systems neuroscience and evolutionary biology. To foster advancing such applications SPP 2205 is offering a series of workshops on advanced data science and inference methods.

• How to learn from complex data I: simulation-based inference in Physics and Life Sciences (Sep 9-10, 2024), Göttingen
  This first workshop introduces the fundamentals of Bayesian inference that are necessary to understand in which sense the parameters of a dynamical process can be empirically inferred, and how to interpret the results of such inference. The course will treat fundamentals and applications of Bayesian networks, MCMC, and nested sampling techniques. Hands-on exercises complement lectures on fundamentals. For nested sampling, a method originally designed for complex system inference in cosmology and astrophysics, the hands-on component will offer interactive application across a set of problems allowing to explore unknown parameter spaces efficiently and to directly estimate the Bayesian evidence for model comparison. For further information follow the link above.