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Current Topics in Computational Neuroscience explores contemporary research questions, methods, and debates in the field of computational neuroscience. This term, students engage with recent literature on the foundations of adaptive and robust behaviour, covering topics that range from neural cell types and circuit dynamics to recurrent neural networks and reinforcement learning. Emphasis is placed on the critical evaluation of current methodologies and emerging trends, equipping students with the conceptual tools needed to interpret and contribute to ongoing research.

The module is aimed at master students and researchers in the field of computational neuroscience. Mathematical skills and a basic familiarity with neuroscientific concepts are advantageous. The course language is English.

Upon completion of the module, students will be able to identify and critically assess relevant contemporary literature on a given topic, and to present their findings effectively in both an oral presentation and a written report.