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@@ -26,24 +26,14 @@ Initiated and curated by [Susanne Wenzel](http://www.fz-juelich.de/SharedDocs/Pe
 
 # Events
 
-### Mehdi Cherti: "Optimization of scientific workflows with machine learning"
+### Talk by Joseph Kambeitz: "Applications of Machine Learning & Computational Modeling in Psychiatry"
 
-Paris Saclay University
-* When: Thursday, 5 December 2019, 2:30pm
-* Where: JSC, Rotunda, building 16.4, room 301
+Department of Psychiatry, University Hospital Cologne
+* When: Thursday, 23 January 2020, 11:00am
+* Where: seminar room of the Institute of Neuroscience and Medicine, building 15.9, Room 4001b
 
-With the advances of machine learning and the acceleration of data generation in scientific domains, bridging the gap between machine learning experts and domain scientists is becoming crucial. In particular, questions such as how to encourage effective collaboration, ensure reproducibility of experiments, make fair and transparent model evaluation are important and challenging. In this talk, I will share my experience in organizing machine learning challenges and contributing to an open source challenge platform that tries to address these important questions. I will describe some challenges (e.g., in environmental science, climate science, analytical chemistry and others) I was involved in, how the code of the participants was modularized to make collaboration easier, and give an overview about the inner workings of the back-end. I will also describe other projects that I worked on and which involved the platform, in particular a project on video object detection and transfer learning.
 
 
-### Talk by [Tim Kietzmann](http://www.timkietzmann.de/): "Deep neural networks as a framework for understanding the dynamic computations of the human visual system"
-
-Donders Institute for Brain, Cognition and Behaviour, Nijmegen
-* When: Tuesday, 2. December 2019, 3:00pm
-* Where: JSC, Rotunda, building 16.4, room 301
-
-This talk will describe our recent methodological advances in understanding information processing in the human brain and artificial vision systems. A central theme of our work is the combination of neuroimaging and deep learning, a powerful computational framework for obtaining models of cortical information processing and task-performing vision systems. Operating in this interdisciplinary research area, I will cover our recent work in which we demonstrated that neural network architectures with recurrent connectivity provide better models of human visual processing (estimated via representational dynamics and behavioural measurements). This insight was made possible by a novel mechanism to directly infuse brain data into large-scale recurrent
-neural networks. In addition, we have shown that recurrent connectivity in artificial vision systems leads to computational benefits. Recurrence enables systems to flexibly trade-off speed for accuracy while exhibiting overall higher object recognition performance. Together, these findings suggest that recurrence is required to capture the representational dynamics of the human visual system.
-
 
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