This will be discussed in the first live session... 

Watch this for an introduction into a topic and an overview over the course. 

This lecture given by Sophie Aerdker gives a brief introduction into foundational concepts from the mathematics of dynamical systems as preparation for the neural dynamics in Dynamic Field Theory, covered in the rest of the course.

The second part of the dynamical systems tutorial presented by Sophie Aerdker as background for the Neural Dynamics course. This covers bifurcations and their significance for modeling.

This Matlab code illustrates the ideas of numerical simulation of differential equations...  As a RUB student you have access to Matlab here. 

Note: this is the most mathematical of all exercises. For mathematically skilled participants, this should not be hard, but hopefully insightful. For those who have not practiced math for a while, this will be hard. But the remainder of the course and of the exercises will NOT be in this style, so do not dispair if you struggle with this exercise sheet. 

This lecture is part of the introductory portion of the course in neural dynamics. It uses the metaphor proposed by Valentino Braitenberg of organisms as vehicles, with sensors, motors, a body that connects these mechanically, and a nervous system that connects these neurally, embedded in a structured environment. We see how behavior emerges from intuitive mental simulation and how this can be made exact based on models of the environment and of the vehicle. This leads to the notion of behavioral attractor dynamics. I discuss the relation to cybernetic thinking and point to how neural dynamics goes beyond this simple case.

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.

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.

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.

This lecture reviews both the memory trace and working memory as two further foundational elements of Dynamic Field Theory. I also introduce briefly the 3-layer field model of change detection and show how this accounts for signatures of visual working memory. The A not B paradigm of Piaget is used to illustrate all these ideas.

This short lecture illustrates how neural dynamic fields can be directly driven from time varying sensory inputs and can conversely drive motor behaviors in closed loop.

A short lecture about how neural dynamic fields are linked to distributions of neural population activation.

This lecture explores neural dynamic fields in higher dimensions. When these dimensions combine different features, they represent bound objects. We show how this enables new cognitive functions, most prominently search, exemplified in visual search. The scaling problem with an increasing number of dimensions is addressed and localist representations are contrasted to distributed representations. The binding through space of Feature Integration Theory provides a solution for our localist approach in DFT. Finally, I show how coordinate transforms are enabled by bound representations and how they are a possible reason for the attentional bottleneck in visual (and other) cognition.

This is an edited version of a video from the 2022 DFT summer school that provides the core ideas around autonnomous generation of sequences of mental or motor states. 

In this lecture, Daniel Sabinasz reviews the famous notion of productivity, namely, the ability to flexibly join “atomic“ linguistic units into “molecular“ linguistic units, and to join molecular linguistic units into more complex molecular linguistic units. He further reviews the notion of compositionality, which accounts for how we understand molecular expressions by virtue of understanding the meanings of their parts and the way that the parts are combined. This leads to a discussion of how compositionality may be achieved by neural systems in a way that is consistent with the principles formalized in Dynamic Field Theory

This tutorial will walk you through the steps of implementing a simple visual search architecture with CEDAR. This is an optional exercise for you and will give you practice in how to build cognitive architectures.

Template for the visual search project