Computational Neuroscience: Vision and Memory

Content

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

Learning outcomes

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.

Teaching format

One unit consists of a lecture of 90 min, self-study time over the week, group work on exercises without teacher for 90 min the week after the lecture, discussion of exercises and general Q&A session with teacher for 90 min right after, followed by the lecture of the next unit.

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.

Exam

The course is concluded with an oral exam (possibly also a digital written exam, if there are many participants, as will be decided within the first two weeks).


Condition for granting the credit points

Passed exam.

Lecturers

Details

Course type
Lectures
Credits
5 CP
Term
Summer Term 2026

Dates

Tutorial
Takes place every week on Tuesday from 12:15 to 13:45 in room NB 3/72.
First appointment is on 21.04.2026
Last appointment is on 21.07.2026
Lecture
Takes place every week on Tuesday from 14:00 to 15:30 in room NB 3/72.
First appointment is on 14.04.2026
Last appointment is on 21.07.2026

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, ...).

About the INI

The Institut für Neuroinformatik (INI) is a research unit of the Faculties of Computer Science and Medicine at the Ruhr-Universität Bochum. Its scientific goal is to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory and 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 psychology and neurophysiology as well as machine learning, neural artificial intelligence, computer vision, and robotics.

Contact

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

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

sekretariat@ini.rub.de
www.ini.rub.de