Computational Neuroscience: Neural Dynamics

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 2019/2020


Takes place every week on Thursday from 14:15 to 16:00 in room NB 3/57.
First appointment is on 17.10.2019
Last appointment is on 30.01.2020
Takes place every week on Thursday from 16:15 to 17:00 in room NB 3/57.
First appointment is on 17.10.2019
Last appointment is on 30.01.2020
Takes place from 14:15 to 16:15 in room NB 2/99.
First appointment is on 20.02.2020
Last appointment is on 20.02.2020


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 corrected and held by Dr. Mathis Richter. 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 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. 


Document Rules for credit
Lecture slides Organizational issues
Lecture slides Introductory lecture
Lecture slides Braitenberg vehicles
Document Introduction: Reading
Lecture slides Dynamical systems tutorial
Lecture slides Introduction to Neural Dynamics
Exercises Exercise 1: Neural dynamics
Document Neural Dynamics: Reading

This is Chapter 1 of the book "Dynamic Thinking: A Primer on Dynamic Field Theory". This is a reading for Exercise 1. 

Lecture slides Lecture on the Foundations of Dynamic Field Theory
Exercises Exercise 2: neural dynamics for XOR
Lecture slides Lecture on the neural grounding of DFT
Exercises Exercise 3 on the neural grounding of DFT
Document Reading for exercise 3
Lecture slides Lecture on Detection Decisions in DFT
Exercises Exercise 4
Document Reading for exercise 4
Lecture slides Lecture on Selection Decisions
Exercises Exercise 5 on Selection Decisions
Document Reading for Exercise 5 on Motor Decisions
Lecture slides Lecture on Memory in DFT
Exercises Exercise 6 (essay!)
Exercises Interactive CEDAR exercise (part 1)
Lecture slides Lecture on higher-dimensional fields
Lecture slides Lecture on Spatial Relations
Exercises Interactive CEDAR exercise (part 2)
Exercises Interactive CEDAR exercise (part 3)
Lecture slides Lecture about sequence generation
Lecture slides Lecture about link of DFT to motor behavior
Document Exam details
Document Bonus points

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