227-0395-00L  Neural Systems

SemesterSpring Semester 2019
LecturersR. Hahnloser, M. F. Yanik, B. Grewe
Periodicityyearly recurring course
Language of instructionEnglish


227-0395-00 VNeural Systems2 hrs
Mon09:15-11:00LFV E 41 »
R. Hahnloser, M. F. Yanik, B. Grewe
227-0395-00 UNeural Systems1 hrs
Mon11:15-12:00LFV E 41 »
15.04.11:15-13:00HG E 26.1 »
11:15-13:00HG E 26.3 »
29.04.11:15-13:00HG E 19 »
11:15-13:00HG E 27 »
R. Hahnloser, M. F. Yanik, B. Grewe
227-0395-00 ANeural Systems1 hrsR. Hahnloser, M. F. Yanik, B. Grewe

Catalogue data

AbstractThis 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.
ObjectiveThis 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.
ContentFrom 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 / NoticeBefore 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 credits6 credits
ExaminersR. Hahnloser, B. Grewe, M. F. Yanik
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examinationwritten 120 minutes
Additional information on mode of examinationThe 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 aidsnone (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.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

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Physics MasterGeneral ElectivesWInformation