Computational Neuroscience: A Lecture Series from Models to Applications

General overview: Anatomical and physiological basis of neuroscience:
- Neuroscience in general, evolution, model systems, general function (sensory organ → CNS → motor response), anatomy (vertebrate/human), general overview of the auditory and visual system and their most important elements.
- neural transmission: resting and action potential, synaptic transmission, neuronal morphology, processing in dendrites, small networks, in vitro electrophysiology

Modeling: Neural dynamics and coding
- Modeling single neurons (classical computational neuroscience, spiking models, current models, firing rate models), or what can math/physics tell us about neurons?
- Populations of neurons; (Sparse) coding, theory of neural networks, associative learning (Hebbian, STDP), reinforcement learning, supervised vs. unsupervised learning
- fundamentals of neuronal signal processing and its modelling; neural encoding/decoding; correlations, reverse correlations, receptive fields; information theory

Towards integration in the nervous system
- Learning and memory: Hippocampal formation, episodic memory and space representation

Engineering for Neuroscience and Neuroprothetics
- Recording neural activity in vivo, multichannel electrophysiology, data acquisition, analysis and interpretation
- Cell-chip interface, network patterning on chip, on-chip neuroscience
- Machine learning and information retrieval in high dimensional data
- Cochlea Implantats: Electric stimulation of the auditory nerve, phenomenological and biophysical models, system overview and stimulation algorithms
- Retina implants and Deep Brain Stimulation
- Key issues in neuro implants

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 systems (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.

Basic knowledge of biology and mathematics recommended.

Video-projection based lecture with practical examples and demonstrations given by experts in each lecture topic.

Lecture with frequent practical demonstrations, notes on the board and video projection, explanations on practical examples, multimedia presentation of important concepts and information.

General textbooks and further reading will be identified at the beginning of the lecture series. Each lecturer will additionally provide optional further reading texts via Moodle.

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.