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Re-Implementing a Dynamic Field Theory Model of Mental Maps using Python and Nengo

Ms. Rabea Turon
Albert-Ludwigs-University Freiburg ~ Computer Science
Ms. Paulina Friemann
University of Freiburg ~ Technical Faculty
Terry Stewart
National Research Council of Canada
Marco Ragni
TU Chemnitz ~ Behavioral and Social Sciences

In Dynamic Field Theory (DFT) cognition is modeled as the interaction of a complex dynamical system. The connection to the brain is established by smaller parts of this system, neural fields, that mimic the behavior of neuron populations. We reimplemented a spatial reasoning model from DFT in Python using the Nengo framework in order to provide a more flexible implementation, and to facilitate future research on a more general comparison between DFT and the Neural Engineering Framework (NEF). Our results show that it is possible to recreate the DFT spatial reasoning model using Nengo, since we were able to duplicate both the behavior of single neural fields and the whole model. However, there are statistical differences in performance between the two implementations, and future work is needed to determine the cause of these differences.


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Cite this as:

Turon, R., Friemann, P., Stewart, T., & Ragni, M. (2020, July). Re-Implementing a Dynamic Field Theory Model of Mental Maps using Python and Nengo. Paper presented at Virtual MathPsych/ICCM 2020. Via