E-learning course overview

This course will be taught online. We work through the present webpages ini.rub.de (see under "E-LEARNING"). This course is not managed through moodle! To register, go to "e-learning" 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. Please fill in your degree program specifically (for example, not just "Master of Science"), so that we know which discipline you come from. 

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. 



Course type
6 CP
Winter Term 2021/2022
e-learning course available


Takes place every week on Thursday from 14:15 to 16:00.
First appointment is on 14.10.2021
Last appointment is on 03.02.2022
Takes place every week on Thursday from 16:15 to 17:00.
First appointment is on 21.10.2021
Last appointment is on 03.02.2022


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.


Exercises are organized by Sophie Aerdker. Details on grading are available in the course rules below.


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. 

Teaching Units

Background reading
Document Introduction to the topic

This is the introduction to the core chapters of the book that forms the backbone of this course. The four chapters described here form the basis for much of the material covered in this course. We move beyond this basis in the second half of the semester by approaching higher cognition. 

Lecture slides Introduction
Video Introduction
Embodied nervous systems: Braitenberg vehicles
Lecture slides Braitenberg vehicles
Video Braitenberg vehicles
Exercises Exercise 2 Braitenberg vehicle
Neural Dynamics
Lecture slides Neural Dynamics
Video Neural Dynamics
Exercises Exercise 3 Neural Dynamics
Lecture slides Neurophysics
Dynamic Neural Fields
Lecture slides DFT: detection decisions
Video DFT: Detection decisions
Exercises Exercise 4 on the detection instability in DFT

This refers to the Chapter 2 of the book available under background reading (top of the list of documents). 

Lecture slides DFT: selection
Video DFT: selection
Exercises Exercise 5 on selection in DFT

This exercise involves a reading: Erlhagen, Schöner: Dynamic Field Theory of Movement Prepartion. Psychological Review 109:545-572 (2002) that is available for download below. 

Document Reading for Exercise 5: Erlhagen Schöner 2002
Lecture slides DFT: Memory
Video DFT memory
Exercises Exercise 6 on working memory in DFT
Dynamic Field Theory and Embodiment
Lecture slides Dynamic Field Theory and embodiment
Video Dynamic Field Theory and embodiment
Neural Basis of DFT
Lecture slides Neural Basis of DFT
Video Neural Basis of DFT (last years lecture)
Review of the foundations of DFT
Exercises Essay exercise "What is Dynamic Field Theory"
Student solutions
Higher-dimensional fields
Video Higher-dimensional fields
Lecture slides Higher-dimensional fields
Grounding of Relational Concepts
Video Grounding spatial relations
Lecture slides Grounding of relational concepts
CEDAR exercises
Exercises Tutorial

Short CEDAR tutorial and introduction to the spatial language architecture. On 27.1.22 we will use this as a basis to build a dynamic field architecture of spatial language. 

Document Template for the CEDAR exercise
Exercises CEDAR exercise part 1
Reference solution Reference Solution (part 1)
Exercises CEDAR exercise part 2
Reference solution Reference Solution (part 2)
Exercises CEDAR exercise part 3
Reference solution Reference Solution (part 3)
Exercises Advanced CEDAR exercise: Visual Search
Exercises Advanced CEDAR exercise: Serial Order

Do the visual search project first


Summary and preparation of oral exam

Lecture slides Summary

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