Computational Neuroscience: Neural Dynamics

This course will be taught in the inverted classroom format combining presence and online features.

For each lecture, a video will be made available that students should watch BEFORE the lecture hour. During the lecture hour, we'll discuss the material presented in the video. So to profit from that, you MUST have seen the video beforehand. That discussion takes place in the classroom, but can also be followed in real time through a Zoom channel. Both students in the class room and students following online should and can ask questions. 

The same format and same Zoom channel will be used for the exercise sessions. In these, the 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 (see under "E-LEARNING"). This course is NOT managed through moodle! Once registered, the e-learning webpages at will give you access to the video lectures, the zoom room, the lecture slides, readings, exercise sheets, and more. You will upload your exercise solutions and will see the marked corrections to your solutions there. You can also ask questions in the "discussion forum". 

To take the course, you must, therefore, register 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 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 2023/2024
e-learning course available


Takes place every week on Thursday from 14:15 to 16:00 in room NB 3/57.
First appointment is on 12.10.2023
Last appointment is on 01.02.2024
Takes place every week on Thursday from 16:00 to 16:45 in room NB 3/57.
First appointment is on 12.10.2023
Last appointment is on 01.02.2024


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 Daniel Sabinasz 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

Lecture slides Introduction
Exercises Math quiz
Lecture slides Braitenberg lecture
Video Braitenberg lecture
Exercises Exercise 2: Braitenberg
Neurophysics tutorial
Lecture slides Neurophysics tutorial
Video Neurophysics tutorial
Neural dynamics
Lecture slides Neural dynamics
Video Neural dynamics

This is the first lecture in the course that introduces neural dynamics properly speaking. Motivated by the dynamics of the membrane potential of neurons, the basic equations is introduced and illustrated. The simplest recurrent network of one neuron coupled excitatorily to itself is used to introduce the detection instability. Two neurons that are inhibitorily coupled exemplify competitive selection.

Exercises Exercise 4: Neural dynamics
Dynamic Field Theory: Foundations
Lecture slides Dynamic Fields: instabilities, dynamic regimes, and detection
Video Dynamic Fields: instabilities, dynamic regimes, and detection

This is the core lecture on Dynamic Field Theory for the Neural Dynamics course. It introduces the notion of a neural dynamic field, making sense of the dimensions over which such fields are defined, and proceeds to discuss the basic attractor states and their instabilities. The detection instability is discussed in some depth and linked to psychophysical evidence.

Exercises Exercise 5: detection
Lecture slides DFT selection
Video DFT selection

This is the second core lecture on Dynamic Field Theory for the Neural Dynamics course. It focusses on selection decisions. I first review how such decisions are made in DFT and what functional properties emerge from that mechanism. Then i discuss the limited evidence we have about "free" choice decisions by reviewing work on saccadic eye movements. The reaction time paradigm is then discussed in light of DFT accounts. Finally, selection decisions in the timed-movement-initiation-paradigm are presented as a major source of empirical support from the DFT framework of selection.

Exercises Exercise 5: selection
Document Background reading for Exercise 6
Lecture slides Memory: Overview
Video Memory: Overview

A brief introduction to memory and how it is addressed in DFT

Lecture slides DFT and memory: full lecture
Exercises Exercise 7: Working memory
Lecture slides Neural foundation of DFT: Overview
Video Neural foundations of DFT: Overview

A brief introduction into how DFT is grounded in neurophysiology through the notion of population distributions of activation (DPA).

Exercises Exercise 8: Essay "What is DFT"?
DFT: Toward higher cognition
Lecture slides Binding
Lecture slides Perceptual grounding of relational concepts
Video Perceptual grounding of relational concepts

This video is only for rehearsal, not a polished lecture video. 

Lecture slides Sequence generation
Video Sequence generation
Introduction to Cedar
Document Cedar tutorial
Document Cedar FAQ
Lecture slides Summary
Document Another review paper

which may serve as a resource for exam preparation


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