Dynamic Field Theory
Dynamic Field Theory (DFT) is a mathematical framework based on the concepts of dynamical systems and the guidelines from neurophysiological findings. Shun-ichi Amari (1977) studied the properties of these networks as a model of the activation observed in populations of neurons from cortical tissue. Fields have the same structure of a recurrent neural network since their connections can have, depending on the relative location within the network, a local excitation or a global inhibition.
Fields are used to represent perceptual features, motion or cognitive decisions, e.g. position, orientation, color, speed. The dynamics of these fields allow the creation of peaks which are the units of representation in DFT (Schöner, 2008). Different configurations of one or
more fields are possible, being the designer responsible for creating a proper connectivity and tuning of parameters. The result of activating this type of network is a continuously adaptive system that responds dynamically to any change coming from external stimuli.Implementations of this framework can be found in the Software section.
Some of the most attractive features of this
approach include the possibility of having a hebbian-type of learning by exploiting the
short-term memory features implicit in the dynamics of this algorithm. Long-term memory, decision making mechanism and noise robustness (also implicit in the dynamics of fields),
and single-shot learning.