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227-0395-00L 6 Credits BSC , MSC , WBZ D-HEST , D-MAVT , D-MATH , D-PHYS , D-INFK , D-ITET
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Neural Systems

Lecturers & Examiners: Prof. Dr. Mehmet Fatih Yanik, Prof. Dr. Benjamin Grewe
VVZ CR 2.0

Last Updated: 2026-06-01 11:33:03

Abstract

This course introduces principles of information processing in neural systems. It covers basic neuroscience for engineering students, experiment techniques used in animal research and methods for inferring neural mechanisms. Students learn about neural information processing and basic principles of natural intelligence and their impact on artificially intelligent systems.

Objective

This course introduces - Basic neurophysiology and mathematical descriptions of neurons - Methods for dissecting animal behavior - Neural recordings in intact nervous systems and information decoding principles - Methods for manipulating the state and activity in selective neuron types - Neuromodulatory systems and their computational roles - Reward circuits and reinforcement learning - Imaging methods for reconstructing the synaptic networks among neurons - Birdsong and language - 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.

General Information

Language
English
Levels
BSC , MSC , WBZ
Frequency
Yearly recurring

Examination

Type
session examination
Mode
written 120 minutes
Aids
none (closed book exam)
The student's final grade is determined by a weighted average: 75% from the written exam grade and 25% from the project/exercise grade (compulsory continuous performance assessment).The project/exercises will be graded individually. If no project/exercise is submitted, this will result in a grade of 1. The total project/exercise grade for the course will comprise a weighted average, i.e. if a project or an exercise spans two lectures it will be weighted double. Students repeating the course can decide at the beginning of the semester if they want to keep the previous grade of their continuous performance assessment (project& exercises).

Course Components

Type Title Time & Place Hours
lecture Neural Systems
  • Mon 09:15-11:00 (ML D 28)
2 h weekly
exercise Neural Systems
  • Mon 11:15-12:00 (ML D 28)
1 h weekly
independent project Neural Systems No time listed 1 h weekly

Offered In