neu241 - Computational Neuroscience - Introduction (Complete module description)

Module label	Computational Neuroscience - Introduction
Module code	neu241
Credit points	12.0 KP
Workload	360 h ( 360 h 2 SWS Lecture Total workload 60h: 30h contact/30h individual revision of lecture contents, test preparation 1 SWS Seminar Total workload 45h: 15h contact/30h individual reading and test preparation 10.5 SWS Supervised exercise Total workload 255h: 145h contact/110h individual work on portfolio tasks (programming, interpretation of simulation results) )
Institute directory	Department of Neurosciences
Applicability of the module	Master's Programme Neuroscience (Master) > Background Modules
Responsible persons	Kretzberg, Jutta (module responsibility) Kretzberg, Jutta (Module counselling) Kretzberg, Jutta (authorised to take exams) Greschner, Martin (authorised to take exams) Ashida, Go (authorised to take exams)
Prerequisites	Programming experience in Matlab (e.g. acquired by a 6 ECTS programming course)
Skills to be acquired in this module	++ Neurosci. knowlg. + Scient. Literature + Social skills ++ Interdiscipl. knowlg ++ Maths/Stats/Progr. + Data present./disc. + Scientific EnglishUpon successful completion of this course, students • are able to implement and apply algorithms in Matlab • have learned to handle scientific data independently • have acquired theoretical and practical knowledge of advanced data analyis techniques • know about computational model approaches on different levels of abstraction • know how to perform model simulations for single cells and small neuronal networks • can interpret simulation results in a neuroscientific context
Module contents	This course consists of six weeks with different topics, which are introduced in lectures, discussed in depth using selected literature in the seminar and consolidated in computer-based hands-on exercises (in Matlab). Portfolio tasks, mainly interpretation of programming results are given every day. Weeks 1 and 2: Spike train analysis response tuning, spike triggered average, receptive fields, linear-nonlinear model, spike correlation, linear reconstruction, classification Weeks 3 and 4: Neuron models Conductance-based single cell models using differerential equations (passive membrane equation, integrate and fire, Hodgkin Huxley, alpha synapses) Weeks 5 and 6: Small network models Feed-forward and feed-back networks, lateral inhibition, central pattern generator, spike-timing dependent plasticity, multi-compartment models
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	annually
Module capacity	18 ( Registration procedure / selection criteria: StudIP; sequence of registration, attandance in pre-meeting Recommended in combination with: neu770 Neuroscientific data analysis in Matlab (prior to the course) neu250 Computational Neuroscience - Statistical Learning (after the course) )

Type of course	SWS	Frequency	Workload of compulsory attendance
Lecture	2	WiSe	28
Seminar	1	WiSe	14
Exercises	10	WiSe	147
Total module attendance time			189 h

Examination	Prüfungszeiten	Type of examination
Final exam of module	during the course	Portfolio, consisting of daily short tests, programming exercises, short reports