
Computational Neuroscience: Neural Dynamics (WT 2021)
Registration deadline: openend
This course will be taught online. We work through the present webpages ini.rub.de (see under "ELEARNING"). This course is not managed through moodle! To register, go to "elearning" and follow the instructions there. You will need an email address of the RuhrUniversity 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 RuhrAlliance, 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 lowdimensional 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 handson 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.
Lecturers
Prof. Dr. Gregor SchönerLecturer 
(+49) 2343227965 gregor.schoener@ini.rub.de NB 3/31 
Sophie Aerdker, M.Sc.Teaching Assistant (primary contact) 
sophie.aerdker@ini.rub.de 
Details
 Course type
 Lectures
 Credits
 6 CP
 Term
 Winter Term 2021/2022
 ELearning
 elearning course available
Dates
 Lecture

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  Exercise

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
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.
Exercises
Exercises are organized by Sophie Aerdker. Details on grading are available in the course rules below.
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.
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. 
Introduction
Lecture slides  Introduction 
Video  Introduction 
Dynamical systems tutorials
Lecture slides  Dynamical Systems Tutorial Part 1 
Video  Dynamical Systems Tutorial Part 1 
Exercises  Exercise 1 Dynamical systems tutorial 
Lecture slides  Dynamical Systems Tutorial Part 2 
Video  Dynamical Systems Tutorial Part 2 
Reference solution  Reference Solution for Exercise 1 
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 
Neurophysics
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 109:545572 (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
Higherdimensional fields
Video  Higherdimensional fields 
Lecture slides  Higherdimensional fields 
Grounding of Relational Concepts
Video  Grounding spatial relations 
Lecture slides  Grounding of relational concepts 
Sequence Generation
Video  Sequence generation (part 1) 
Lecture slides  Sequence generation (part 1) 
Video  Sequence generation (part 2  starts from minute 17) 
Document  Sequence generation (part 2) 
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 
The Institut für Neuroinformatik (INI) is a central research unit of the RuhrUniversität 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
D44801 Bochum, Germany
Tel: (+49) 234 3228967
Fax: (+49) 234 3214210