Dynamic control using feedforward networks with adaptive delay and facilitating neural dynamics

Time delays are universal in an organism's nervous system. A majority of them are the results of the limited propagation speed of action potential through the axons. They are inevitable and commonly considered as obstacles to overcome. However, many studies have shown that delays in the nervous system have a nonuniform distribution which helps stabilize the dynamics of the network, leads to greatly increased information capacity, and enable the emergence of the brains predictive function. Additionally, our previous work indicates that the brains predictive function may utilize facilitating neuronal dynamics to generate short-term plasticity (decrease or increase in synaptic transmission) for delay compensation purposes. In this study, we demonstrate how adaptive synaptic delay, together with facilitating neuronal dynamics, can be used to build a sensorimotor controller for a dynamic control task by utilizing simple feedforward neural networks, all under impoverished and long delayed input conditions. Our findings confirm that through adaptive delay and facilitating neuronal dynamics, feedforward neural networks develop a strong memory-like mechanism and exhibit rich dynamic behaviors, successfully solving a tough dynamic control task. We expect our results to shed new light on the role of adaptive synaptic delay and facilitating dynamics in the nervous system, in relation to memory-like mechanisms.