E-learning course overview
  • Computational Neuroscience: Neural Dynamics (WT 2024)
    Registration deadline: open-end

    Prof. Dr. Gregor Schöner

    To take this course, you must register following the instructions on this page. You will need an email address of the Ruhr-University or the Technical University Dortmund for registration. If you are an exchange student without such an email address or come from another university within the Ruhr-Alliance, contact us by email as instructed there. When registering, please fill in your degree program (for example, "MSC Angewandte Informatik", not just "Master of Science"). This is important information for us to manage exams and credit points. 

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This course will be combine presence and online features ("hybrid"). 

There will be elements of the "inverted classrooom" in which short videos will be made available that students should watch BEFORE the lecture hour. During the lecture hour, an in-depth presentation and discussion of the material will be done. The lectures and exercises will take place in the classroom, but can also be followed in real time through a Zoom channel. 

In the exercise sessions, solutions of the corrected exercises will be discussed. The exercise session can also be used to ask general questions. 

The course uses e-learning features provided through the present webpages. This course is NOT managed through moodle! To take the course, you must registerm, therefore, through this webpage: Go to "e-learning", select this course, and follow the instructions there. You will need an email address of the Ruhr-University or the Technical University Dortmund for registration. If you are an exchange student without such an email address or come from another university within the Ruhr-Alliance, contact us by email as instructed there. When registering, please fill in your degree program (for example, "MSC Angewandte Informatik", not just "Master of Science"). This is important information for us to manage exams and credit points. 

This course lays the foundations for a neurally grounded understanding of the fundamental processes in perception, in cognition, and in motor control, that enable intelligent action in the world. The theoretical perspective is aligned with ideas from embodied and situated cognition, but embraces concepts of neural representation and aims to reach higher cognition. Neural grounding is provided at the level of populations of neurons in the brain that form strongly recurrent neural networks and are ultimately linked to the sensory and motor surfaces.

The theoretical concepts on which the course is based come form dynamical systems theory. These concepts are used to characterize neural processes in strongly recurrent neural networks as neural dynamic systems, in which stable activation states emerge from the connectivity patterns within neural populations. These connectivity patterns imply that neural populations represent low-dimensional features spaces. This leads to neural dynamic fields of activation as the building blocks of neural cognitive architectures. Dynamic instabilities induce change of attractor states from which cognitive functions such as detection, change, or selection decisions, working memory, and sequences of processing stages emerge.

The course partially follows a textbook (Dynamic Thinking—A primer on Dynamic Field Theory, Schöner, Spencer, and the DFT research group. Oxford University Press, 2016), of which chapters will serve as reading material. Exercises will focus on hands-on simulation experiments, but also involve readings and the writing of short essays on interdisciplinary research topics. See www.dynamicfieldtheory.org for some of that material. Tutorials on mathematical concepts are provided, so that training in calculus and differential equations is useful, but not a prerequisite for the course. 

Passing the final exam is required to receive credit. The mark will be enhanced by bonus points earned by submitting homework including an essay in mid-term

Lecturers

Details

Course type
Lectures
Credits
6 CP
Term
Winter Term 2024/2025
E-Learning
e-learning course available

Dates

Lecture
Takes place every week on Thursday from 14:15 to 16:00 in room NB 3/57.
First appointment is on 10.10.2024
Last appointment is on 30.01.2025
Exercise
Takes place every week on Thursday from 16:15 to 17:00 in room NB 3/57.
First appointment is on 10.10.2024
Last appointment is on 30.01.2025

Requirements

This course requires some basic math preparation, typically as covered in two semesters of higher mathematics (functions, differentiation, integration, differential equations, linear algebra). The course does not make extensive use of the underlying mathematical techniques, but uses the mathematical concepts to express scientific ideas. Students without prior training in the relevant mathematics may be able to follow the course, but will have to work harder to familiarize themselves with the concepts.

Literature

The course will be based on selected chapters of a textbook (Dynamic Thinking: A Primer on Dynamic Field Theory by Schöner, G., Spencer, J, and the DFT Research Group, Oxford University Press). The Introduction and the first two chapters are available for download in the course materials below. These and others will also serve as readings for some of the exercises. 

For the mathematical background in dynamical systems an excellent resource is a book that is available online as a free download (thanks to the author's generosity): Edward R. Scheinerman's Invitation to Dynamical Systems.  This book covers both discrete and continuous time dynamical systems, while in the course we will only make use of continuous time dynamical systems formalized as differential equations. 

Documents

Document Background reading: Overall approach

This chapter from the DFT book gives a brief motivation and surveys the 4 foundational chapters that will from the first half of the course. 

About the INI

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.

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