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

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.