Moses - Models of Higher Brain Functions

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Module / Version:
#40808 / #3

Validity:
Seit SoSe 2021

Default display language:
English

Module title:
Models of Higher Brain Functions - Introduction

Credits:
6

Responsible person:
Sprekeler, Henning

Grading:
benotet

Type of exam:
Mündliche Prüfung

Zugehörigkeit

Faculty:
Fakultät IV

Institute:
Institut für Softwaretechnik und Theoretische Informatik

Area of expertise:
34352100 FG Modellierung kognitiver Prozesse

Prüfungsausschuss:
No information

Kontakt

Office:
MAR 5-3

Contact person:
Sprekeler, Henning

E-mail address:
graduateprograms@bccn-berlin.de

Website:

https://www.bccn-berlin.de/courses-and-modules.html#ui-id-11

Learning Outcomes

Having completed this module, participants will know: - the state-of-the-art models in these domains and their theoretical foundations. They will understand: - strengths and limitations of the different modeling approaches (e.g. bottom-up versus top-down) - the rationale behind models and their implementation - performance criteria and critical statistical tests. They will be able to: - modify models of cognitive processes - apply existing models to novel experimental paradigms, situations or data.

Content

Theoretical Lecture & Analytic Tutorial: Computational models of - visual processing - attention - multisensory integration - decision making - behavioral learning (conditioning, reward learning) - motor control Programming Tutorial: - hands-on experience of the models covered in the lecture, by means of computer simulations in Python.

Module Components

Pflichtgruppe:

All Courses are mandatory.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Theoretical Lecture	VL		SoSe	No information	2

Tutorials:

1 from the following courses must be completed.

Course Name	Type	Number	Cycle	Language	SWS	VZ
Analytical Tutorial	TUT		SoSe	No information	2
Programming Tutorial	TUT		SoSe	No information	2

Workload and Credit Points

Analytical Tutorial (TUT):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Pre/post processing	15.0	6.0h	90.0h
			120.0h(~4 LP)

Programming Tutorial (TUT):

Workload description	Multiplier	Hours	Total
Attendance	15.0	2.0h	30.0h
Pre/post processing	15.0	6.0h	90.0h
			120.0h(~4 LP)

Theoretical Lecture (VL):

Workload description	Multiplier	Hours	Total
Präsenzzeit	15.0	2.0h	30.0h
Vor-/Nachbereitung	15.0	2.0h	30.0h
			60.0h(~2 LP)

The Workload of the module sums up to 180.0 Hours. Therefore the module contains 6 Credits.

Description of Teaching and Learning Methods

The lecture part consists of teaching in front of the class. Participants are expected to rehearse topics before class using the recommended literature. In preparation for the exercises and tutorials they use, in addition, their class notes. Homework assignments are given on a regular basis, and must usually be solved within one week. These assignments cover analytical & mathematical exercises (Analytical Tutorial) as well as numerical simulations & programming exercises (Programming Tutorial). Working in small groups of two to three students is encouraged. Homework assignments and their solutions are discussed during the tutorial. In addition, selected topics presented during the lecture are rehearsed by the tutor as needed.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

- mathematical knowledge: Some acquaintance with analysis, linear algebra, probability calculus and statistics - basic knowledge about neurobiology and cognitive psychology - basic programming skills, preferably some knowledge of Python - good command of the English language

Mandatory requirements for the module test application:

1. Requirement

Programming Tutorial: complete 60% of the programming assignments

Analytical Tutorial: gain at least 60% of the points in the homework assignments

Module completion

Grading

graded

Type of exam

Oral exam

Language

English

Duration/Extent

30 min

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:
Sommersemester.

Maximum Number of Participants

The maximum capacity of students is 30.

Registration Procedures

Students must enroll per e-mail (to: graduateprograms(at)bccn-berlin.de) before the fourth (4th) lecture took place. Registration must include the following information: name, email, study program and university, matriculation number, module components to be taken. Students of the Master program in Computational Neuroscience have to register for the final oral exam at least three working days prior to the examination date. Registration has to be done with the examination office (Prüfungsamt) of TU Berlin. For students from other programs, other regulations may apply. Please consult the examination regulations (Prüfungsordnung) of your program. Note that the total number of participants in the three variants of this module (MHBF, MHBF: Theory and Simulation, MHBF: Introduction) is limited to 30 participants. Preference is given to students in the MSc Computational Neuroscience, for whom the module is mandatory.

Recommended literature
"Cognitive Neuroscience - The Biology of the Mind", Gazzaniga, Ivry, Mangun
"Essentials of Cognitive Neuroscience", Postle
"The Student's Guide to Neuroscience", Ward

Assigned Degree Programs

Semester:

Zeige auch ausgelaufene Studiengänge und StuPOs

This module is used in the following Degree Programs (new System):

Studiengang / StuPO	StuPOs	Verwendungen	Erste Verwendung	Letzte Verwendung
Computer Engineering (M. Sc.)	1	14	SoSe 2021	SoSe 2024
Computer Science (Informatik) (M. Sc.)	1	14	SoSe 2021	SoSe 2024
Elektrotechnik (M. Sc.)	1	7	SoSe 2021	SoSe 2024
Wirtschaftsingenieurwesen (M. Sc.)	1	14	SoSe 2021	SoSe 2024

Miscellaneous

No information

Models of Higher Brain Functions - Introduction