227-0395-00L Neural Systems
| Semester | Spring Semester 2019 |
| Lecturers | R. Hahnloser, M. F. Yanik, B. Grewe |
| Periodicity | yearly recurring course |
| Language of instruction | English |
Courses
| Number | Title | Hours | Lecturers | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 227-0395-00 V | Neural Systems | 2 hrs |
| R. Hahnloser, M. F. Yanik, B. Grewe | |||||||||||||||
| 227-0395-00 U | Neural Systems | 1 hrs |
| R. Hahnloser, M. F. Yanik, B. Grewe | |||||||||||||||
| 227-0395-00 A | Neural Systems | 1 hrs | R. Hahnloser, M. F. Yanik, B. Grewe |
Catalogue data
| Abstract | This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experimental techniques used in studies of animal behavior and underlying neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on efforts to design artificially intelligent systems. |
| Learning objective | This course introduces - Methods for monitoring of animal behaviors in complex environments - Information-theoretic principles of behavior - Methods for performing neurophysiological recordings in intact nervous systems - Methods for manipulating the state and activity in selective neuron types - Methods for reconstructing the synaptic networks among neurons - Information decoding from neural populations, - Sensorimotor learning, and - Neurobiological principles for machine learning. |
| Content | From active membranes to propagation of action potentials. From synaptic physiology to synaptic learning rules. From receptive fields to neural population decoding. From fluorescence imaging to connectomics. Methods for reading and manipulation neural ensembles. From classical conditioning to reinforcement learning. From the visual system to deep convolutional networks. Brain architectures for learning and memory. From birdsong to computational linguistics. |
| Prerequisites / Notice | Before taking this course, students are encouraged to complete "Bioelectronics and Biosensors" (227-0393-10L). As part of the exercises for this class, students are expected to complete a (python) programming project to be defined at the beginning of the semester. |
Performance assessment
| Performance assessment information (valid until the course unit is held again) | |
Performance assessment as a semester course | |
| ECTS credits | 6 credits |
| Examiners | R. Hahnloser, B. Grewe, M. F. Yanik |
| Type | session examination |
| Language of examination | English |
| Repetition | The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling. |
| Mode of examination | written 120 minutes |
| Additional information on mode of examination | The student's grade is composed 3/4 by final exam and 1/4 by project (compulsory continuous performance assessment). The project will be graded, if no project is submitted, this will result in a grade of 1. |
| Written aids | none (closed book exam) |
| This information can be updated until the beginning of the semester; information on the examination timetable is binding. | |
Learning materials
| No public learning materials available. | |
| Only public learning materials are listed. |
Groups
| No information on groups available. |
Restrictions
| There are no additional restrictions for the registration. |
