Computational Neuroscience: Vision and Memory



Course type
5 CP
Summer Term 2022
moodle course available


Takes place every week on Tuesday from 10:30 to 12:00 in room ID 03/419.
First appointment is on 05.04.2022
Last appointment is on 12.07.2022
Takes place every week on Tuesday from 12:15 to 13:45 in room ID 03/419.
First appointment is on 05.04.2022
Last appointment is on 12.07.2022
Takes place on 02.08.2022 from 11:00 to 13:00 in room ET/IT CIP-Pool 2 (ID 03/121).
Takes place on 27.09.2022 from 11:00 to 13:00 in room ET/IT CIP-Pool 2 (ID 03/121).

Enrollment: To enroll in this course with me, you just have to enroll in the Moodle course and participate. You also might have to enroll with your examination office, but that is something you have to figure out yourself.

Credits: 5 CP

Workload: 150 h

Semester: any semester Master

Cycle (Turnus): each SS

Duration (Dauer): 1 semester

Contact time (Kontaktzeit): 4 SWS (60 h)

Self studies (Selbststudium): 90 h

Group size (Gruppengröße): ca 20

Language: English.

Requirements: The mathematical level of the course is mixed but generally high. The tutorial is almost entirely mathematical. Mathematics required include calculus (functions, derivatives, integrals, differential equations, ...), linear algebra (vectors, matrices, inner product, orthogonal vectors, basis systems, ...), and a bit of probability theory (probabilities, probability densities, Bayes' theorem, ...).

Learning outcomes (Lernziele): After the successful completion of this course the students

  • know basic neurobiological facts about the visual system and the hippocampus,
  • know a number of related models and methods in computational neuroscience,
  • understand the mathematics of these methods,
  • can communicate about all this in English.

Content (Inhalt): This lecture covers basic neurobiology and models of selforganization in neural systems, in particular addressing

  • Learning and self-organization
    • Hebbian Learning
    • Neural learning dynamics and constrained optimization
    • Dynamic field theory
  • Vision 
    • Receptive fields
    • Neural maps
  • Hippocampus
    • Navigation
    • Episodic memory
    • Hopfield Network

Teaching format (Lehrformen): This course is given with the flipped/inverted classroom concept. First, the students work through online material by themselves. In the lecture time slot we then discuss the material, find connections to other topics, ask questions and try to answer them. In the tutorial time slot the newly acquired knowledge is applied to analytical exercises and thereby deepened. I encourage all students to work in teams during self-study time as well as in the tutorial.

Exam (Prüfungsformen): The course is concluded with a digital written exam for 90 minutes within a 120 minutes time slot. We offer two dates in the semester of the course and none in the next semester. You are free to pick either of the two dates, but if you pick the second and you fail, the next opportunity to retry the exam is only about one year later. This is an open book exam, thus you may use any tools (e.g. lecture notes or Wikipedia) except communication with other people. Registration for the exam with us happens at the end of the course, you will receive instructions on that in due time. You might also have to register for this exam in your examination office, but this is something you'll have to figure out yourself. Registering with us and with the examination office are independent of each other, and if possible we will report your grade to the examination office in any case, whether you have registered there or not. There are no prequisits for the exam, like 50% points in tutorials or the like.

Condition for granting the credit points (Voraussetzungen für die Vergabe von Kreditpunkten): Passed written exam.

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

Universitätsstr. 150, Building NB, Room 3/32
D-44801 Bochum, Germany

Tel: (+49) 234 32-28967
Fax: (+49) 234 32-14210