Computational Neuroscience: A Lecture Series from Models to Applications

Topics

A. General overview: Anatomical and physiological basis of neuroscience

• Motivation for doing computational Neuroscience; Neuroanatomy primer: General layouts of nervous systems, overview of the human brain and forebrain, morphology of neurons, visual and auditory pathways
• Neurophysiology primer: Basic biology of neurons, resting and acting potentials, synaptic transmission, plasticity of neuronal connections, dendritic processing

 B. Modeling: Neural dynamics and coding

• Modeling dynamics and computations of single neurons
• Theory of neural networks and learning
• Deep Learning

 C. Towards integration in the nervous system

• Systems mechanisms of learning and memory: theory, methods and their application
• Visual system I: neurobiology
• Visual system II: computation
• Spatial perception

 D. Engineering for Neuroscience and Neuroprosthetics

• Neuroprosthetics I: Cochlea Implants: System overview and stimulation algorithms
• Neuroprosthetics II: Cochlea Implants: Electric stimulation of the auditory nerve, phenomenological models
• Engineering applications of brain models

Updates of the teaching content and further material can be found on the BCCN page.

Further information

Students take part in the lecture and additionally learn the course content during self study with the materials provided by the lecturers (handouts, further reading advice). The lecture, providing an overview of the various aspects pertaining to computational neuroscience, will be presented by several experts in their respective fields. 
Knowledge-based learning outcomes and understanding of the course content, from the neurobiological foundation, the mathematical tools, principles of auditory prostheses to techniques to measure physiological responses will be assessed in a 60 min written examination with questions set and corrected by the respective lecturers. The exam will also assess the ability to solve general (practical) problems  in order to test the ability to transfer knowledge.

Basic knowledge of biology and mathematics recommended.

This interdisciplinary lecture series taught by neurosience experts from TUM and LMU provides an introduction to computational neuroscience. After taking part in this course, students are familiar with basic neuroanatomy and the neural processes in different sensory system (visual, auditory). Students will have learnt the fundamental methods for modelling neural behaviour on the cell and the systemic level and how data to fit those models can be obtained from experiments. Additionally, students will have understood how such models can be used for engineering technical applications involving neural systems, like Neuroprostheses.