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 elearning features provided through the present webpages (see under "ELEARNING"). This course is NOT managed through moodle! Once registered, the elearning webpages at ini.rub.de 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 "elearning", select this course, 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 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 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 
Daniel Sabinasz, M.Sc.Teaching Assistant (primary contact) 
(+49) 2343227973 daniel.sabinasz@ini.rub.de NB 02/74 
Lukas BildheimTutor 
(+49) 2343227971 lukas.bildheim@ini.rub.de NB 02/76 
Dr. Lei ZhangLecturer 
(+49) 2343227971 lei.zhang@ini.rub.de NB 02/76 
Details
 Course type
 Lectures
 Credits
 6 CP
 Term
 Winter Term 2023/2024
 ELearning
 elearning course available
Dates
 Lecture

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  Exercise

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
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 Daniel Sabinasz 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
Introduction
Lecture slides  Introduction 
Exercises  Math quiz 
Dynamical Systems Tutorial
Video  Dynamical Systems Tutorial Part 1 
Video  Dynamical Systems Tutorial Part 2 
Document  Dynamical Systems Tutorial  Slides 
Exercises  Exercise 1 
Braitenberg
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 timedmovementinitiationparadigm 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 
Summary
Lecture slides  Summary 
Document 
Another review paper
which may serve as a resource for exam preparation 
Documents
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