Course 2023-2024 a.y.

• Principles of neural coding
• Biophysics governing neurons and synapses
• Modeling individual neuron activity
• Dynamics of networks of neurons
• Learning in neural circuits

• Grasp fundamental neurobiological concepts
• Interpret experimental findings obtained through recent technologies
• Comprehend phenomenological models describing neurobiological processes
• Understand mechanistic models explaining the neural underpinnings of brain functions
• Explore normative models outlining the principles guiding neurobiological processes

• Interpret experimental data derived from neurobiological recordings
• Calculate response dynamics within single neuron, synapse, and neural network models
• Analyze learning and learning dynamics in neural models

• Face-to-face lectures
• Online lectures
• Exercises (exercises, database, software etc.)
• Group assignments

• Face-to-face lectures focus on the theoretical, methodological, and computational aspects of the topics covered by the course.
• During hands-on exercise sessions, online lectures, and group projects, the primary focus will be guiding students through accessing, analyzing, and interpreting modern neuroscience datasets. These datasets encompass large-scale recordings from awake behaving animals, aiming to deepen understanding through practical application and collaborative exploration.

• The written exam will test the students’ understanding of the concepts taught in class.

• The group assignment will test the students’ ability to apply these concepts to specific neurobiological problems.

• Grading scheme:

  • General written exam: 50% of the final grade. 

  • Group assignment: 50% of the final grade.

The recommended textbook is:

• L. F. Abbott and P. Dayan, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, The MIT Press, 2005. Additional relevant references will be provided during the course.