Compensating for Neural Transmission Delay Using Extrapolatory Neural Activation in Evolutionary Neural Networks

In an environment that is temporal as well as spatial in nature, the nervous system of agents needs to deal with various forms of delay, internal or external. Neural (or internal) delay can cause serious problems because by the time the central nervous system receives an input from the periphery, the environmental state is already updated. To be in touch with reality in the present rather than in the past, such a delay has to be compensated. Our observation is that facilitatory dynamics found in synapses can effectively deal with delay by activating in an extrapolatory mode. The idea was tested in a modified 2D pole-balancing problem which included sensory delays. Within this domain, we tested the behavior of recurrent neural networks with facilitatory neural dynamics trained via neuroevolution. Analysis of the performance and the evolved network parameters showed that, under various forms of delay, networks utilizing facilitatory dynamics are at a significant competitive advantage compared to networks with other dynamics. Keywords– Neural delay, delay compensation, extrapolation, pole balancing, evolutionary neural networks