Computational Neuroscience: A Lecture Series from Models to Applications
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.
Module version of SS 2015
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
Whether the module’s courses are offered during a specific semester is listed in the section Courses, Learning and Teaching Methods and Literature below.
|available module versions
EI7646 is a semester module in German or English language at Master’s level which is offered every semester.
This Module is included in the following catalogues within the study programs in physics.
- Catalogue of non-physics elective courses
Content, Learning Outcome and Preconditions
- 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
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|Computational Neuroscience: A Lecture Series from Models to Applications
Learning and Teaching Methods
Description of exams and course work
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.
There is a possibility to take the exam in the following semester.