neu242 - Computational Neuroscience - Encoding and Decoding (Complete module description)

Module label	Computational Neuroscience - Encoding and Decoding
Module code	neu242
Credit points	6.0 KP
Workload	180 h
Institute directory	Department of Neurosciences
Applicability of the module	Master's Programme Neuroscience (Master) > Background Modules
Responsible persons	Greschner, Martin (module responsibility) Clemens, Jan (authorised to take exams) Greschner, Martin (authorised to take exams) Greschner, Martin (Module counselling)
Prerequisites	Enrolment in Master program Neuroscience; Students from other study programs are welcome if space is available.This module requires good programming skills! (As taught in neu710 or neu715.)
Skills to be acquired in this module	Upon completion of this module, students - are able to implement and apply algorithms in Matlab or Python - have learned to handle scientific data independently - have acquired theoretical and practical knowledge of advanced data analysis techniques- can interpret simulation results in a neuroscientific context Skills to be acquired/ competencies: ++ Neuroscience knowledge + Scientific Literature + Social skills ++ Maths/Stats/Programming + Data presentation/discussion + Scientific English
Module contents	This course consists of three weeks full-time work on the topics encoding and decoding of spike trains, which are introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises (in Matlab or Python). Portfolio tasks consists of programming and the interpretation of the analyses. Specific topics: response tuning, spike triggered average, receptive fields, linear-nonlinear model, spike correlation, linear reconstruction, classification
Recommended reading	Skripts for each course day will be provided prior to / during the course. Copies of scientific articles for the seminar and as basis for portfolio assignments will be provided prior to the course. Recommended textbooks or other literature: Dayan / Abbott: Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT Press (More text book chapters will be suggested prior to the course).
Links
Language of instruction	English
Duration (semesters)	1 Semester
Module frequency	Annualy, second half of winter term (December to early January)
Module capacity	18

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	WiSe	28 Contact (hours): 28 Self-study and preparation for exam (hours): 32 Total Workload (hours): 60
Exercises	4	WiSe	56 Contact (hours): 56 Self-study and preparation for exam (hours): 64 Total workload (hours): 120
Total module attendance time			84 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	During the course (assignment tasks)	Portfolio, consisting of short tests, programming tasks, and interpretation of modeling / data analysis results.