Tantárgy adatlapja
Machine learning and computer science has a lot to learn from the brain when it comes to efficiency, robustness, generalisation and adaptivity, yet the code and the algorithms running on the neural hardware are poorly understood. Using the state of the art electrophysiological and optical techniques we are now able to monitor the activity of large number of neurons in behaving animals providing us an unprecedented opportunity to observe how interacting neural populations give rise to computation.
The aim of the course is to introduce students to recent approaches for analysing and interpreting neuronal population activity data. We will focus on generative models and take Bayesian perspective: we will learn how to build probabilistic models of the data and how to perform inference and learning using these models. The course is a mixture of lectures focusing the theoretical background, discussing neuroscience experiments and practical sessions where students will apply the learned techniques to real neuronal data. Interactions between the students is highly encouraged.
Further Information: https://koki.hun-ren.hu/researchgroups/biological-computation/dissemination-education/courses/ml4nda
