This first of three core lectures of the DFT summerschool introduces the foundational ideas of neural dynamics and dynamic neural fields. The core are the attractor states and their characteristic instabilities for detection, selection, and memory. A case study illustrates how DFT models are used to account for behavioral and neural data, a second case study illustrates how DFT models ultimately link to sensory and motor systems.

This second core lecture of the DFT school looks into dynamic neural fields that combine different types of feature dimension and provide the basis for flexibly extracting and binding features, enable visual search. Binding through space enables higher-dimensional representations within the localist neural representations of DFT. Coordinate transforms exploit the binding of source and target coordinate frames to a space that steers the coordinate transform. Two examples illustrate how higher cognition emerges: Perceptual grounding and mental mapping of relational phrases. 

This third core lecture discusses the mechanism in neural dynamics for the autonomous generation of sequences of behavioral or mental acts. This is followed by a general discussion: Why DFT architectures work as promised; The role of embodiment in DFT; The relationship between DFT and connectionism/deep neural network, between DFT and symbolic computation,  and between DFT and Vector-Symbolic-Architectures.

Jan Tekülve and Daniel Sabinasz present the software frameworks Cedar and DFF for DFT simulations. 

Tutorial and Exercise with the Cedar Simulator

In this special lecture, Raul Grieben presents a part of his work on a neural theory of visual search.His presentation emphasizes how a deep neural network that supports classification is mapped onto the localist representations of DFT and how guidance features for visual search can be extracted from the deep network. He demonstrates how the model accounts for scene grammar by explaining how visual anchors work.

In this special lecture, Daniel Sabinasz reviews the notion of grounded cognition. He points to the challenges that a neural theory of grounding must face and outlines the DFT approach toward solving these problems. The high point of his talk is an outline of his own recent theoretical work on neural representations for conceptual structure and an account for the grounding of nested relations.

In this Guest Lecture, Mathis Richter shows how neuromorphic computing can be used to implement DFT architectures in form of genuinely neural computation. His emphasis is on architectures that link to sensory inputs, but he reviews neuromorphic computation more broadly as well.

In this shorter presentation, Cora Hummert reviews an experimental study that was inspired by the DFT accounts for visual search and for reaching movements outlined earlier in the course and, for some of the participants, studied in the projects workshop. Within a visual search task, participants moved a computer mouse to point at an object presented at varied locations that matched the search cues, color and orientation. A distractor object that only matched color attracted the path of the mouse. 

(The Google doc with live videos is here.) In this final special lecture of the DFT summer school 2022, Jan Tekülve introduces the notion of "intentionality" as defined by philosophers of mind as a way to characterize what a neurally grounded account for the "mind" needs to achieve. He then provides a neural process model within DFT that realizes intentionality in the six psychological modes: perception, memory, beliefs, and action, planning, goals that sample the two directions of fit of intentionality, mind-to-world and world-to-mind. He makes this concrete around a simple toy scenario in which a robotic vehicle explores an environment, of which it builds a model, and in which it transforms objects by "painting" (in a highly simplified way). The robot learns contingencies of the world (of color combination in painting over colored surfaces), which it uses to achieve its goal.

Jan Tekülve and Daniel Sabinasz present the projects that participants can do in Cedar, Covina, or DFF during the workshop poertion of the summer school.

All Cedar projects should start with this project

Might only run on Windows, try the template before starting

Template Architecture files for all Cedar Projects. The reaching project needs the _simulator file, while all other projects can be done with the _picture file.