
Computational Neuroscience: Neural Dynamics (WT 2020)
Registration deadline: openend
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) 
(+49) 2343224201 sophie.aerdker@ini.rub.de NB 02/73 
Dr.Ing. Mathis RichterTeaching Assistant 
mathis.richter@ini.rub.de 
Details
 Course type
 Lectures
 Credits
 6 CP
 Term
 Winter Term 2020/2021
 ELearning
 elearning course available
Dates
 Lecture

Takes place
every week on Thursday from 14:15 to 16:00 in an online live session in the elearning course.
First appointment is on 29.10.2020
Last appointment is on 11.02.2021  Exercise

Takes place
every week on Thursday from 16:15 to 17:00 in an online live session in the elearning course.
First appointment is on 29.10.2020
Last appointment is on 11.02.2021
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 Aerdtker and Dr. Mathis Richter. 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 DFT Primer 
Document  Chapter 1 of DFT Primer: Neural Dynamics 
Document  Chapter 2 of DFT Primer: Dynamic Fields 
Welcome and Introduction
Lecture slides 
Introduction
Brief introduction to the course 
Video  Introduction lecture video 
Dynamical systems tutorials
These short tutorials are outside the main syllabus of the lecture course. They serve to provide background to the mathematical concepts used. A resource is the book by Scheinerman cited on the course's homepage.
Lecture slides 
Dynamical systems tutorial 1
about the basic concepts of dynamical systems. 
Video  Dynamical systems tutorial 1 video 
Lecture slides 
Dynamical systems tutorial 2
about numerical solutions of differential equations 
Video  Dynamical systems tutorial 2 video 
Document 
Code sample
for the numerical solution of differential equations 
Lecture slides 
Dynamical systems tutorial 3
about attractors and instabilities 
Video 
Dynamical systems tutorial 3
about attractors and bifurcations 
Exercises  Exercise 1 
Reference solution 
Exercise solution and remarks
This is not a solution as we would have expected it from you but rather a document that explains a possible solution. 
Braitenberg vehicles: Embodied nervous systems
Lecture slides  Lecture on Braitenberg vehicles 
Link 
A websimulator for Braitenberg vehicles
provided by Harmen de Weerd, a dutch researcher. 
Video  Lecture on Braitenberg vehicles: video 
Neural dynamics
Introduction into the foundations of neural dynamic thinking. Chapter 1 of the DFT primer (see background material) is useful reading for this.
Lecture slides  Neural dynamics 
Video  Neural dynamics 
Exercises  Neural dynamics 
Neural basis of dynamic fields
Lecture slides  Neural basis of dynamic fields 
Video  Neural basis of dynamic fields 
Exercises  Neural basis of dynamic fields 
Dynamic Field Theory: Detections
Lecture slides  Detection in DFT 
Video  Detection in DFT 
Exercises  Detection in DFT 
Dynamic Field Theory: Selection
Lecture slides  Selection in DFT 
Video  Selection in DFT 
Exercises  Selection in DFT 
Document  Reading for Exercise 5 
Dynamic Field Theory: Memory
Lecture slides  Dynamic Field Theory: Memory 
Video  Dynamic Field Theory: Memory 
Exercises  Dynamic Field Theory: Memory 
Dynamic Field Theory and embodiment
Linking to sensory inputs and linking to motor output, with issues of normalization and coordinate frames
Lecture slides  Dynamic Field Theory and embodiment 
Video  Dynamic Field Theory and embodiment 
Review of the foundations of DFT
Exercises 
Essay exercise (exercise 7)
This essay exercise reviews the foundations of DFT 
Higherdimensional fields
Lecture slides  DFT in higher dimensions 
Video  DFT in higher dimensions 
Interactive CEDAR exercises
Exercises  CEDAR Tutorial 
Video  Video dataset 
Exercises  CEDAR Exercise (Part 1) 
Document  CEDAR Exercise Architecture Template (Part 1) 
Exercises  CEDAR Exercise (Part 2) 
Exercises  CEDAR Exercise (Part 3) 
Concepts and relations
Lecture slides  Grounding of relational concepts 
Video  Grounding of relational concepts 
Lecture slides  Mental mapping and inference 
Video  Mental mapping and inference 
Sequence generation
Lecture slides  Sequence generation part 1 
Video  Sequence generation part 1 
Lecture slides  Sequence generation part 2 
Video  Sequence generation part 2 
Intentional systems
Lecture slides  Neural dynamic concepts for intentional systems 
Video  Intentional systems 
Documents
Document 
Rules
for how exerices can be used to obtain bonus points, for passing the exam, and for how the final grade is computed 
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