Timing is important: delaying action execution in Plastic Neural Networks

Plastic Neural Networks (PNNs) are known for their ability to adapt to environmental changes. It is generally believed that PNNs cannot solve timing tasks which require a predefined delay before execution of an action. In this study we investigate the ability of PNNs to solve timing tasks. Our experiments evolve PNNs to perform successfully on a task requiring the delayed execution of an action. The results of our experiments show that PNNs are capable of solving the timing task. We analyse the underlying mechanism and find it is based on slow neural activation dynamics. The mechanism is discussed in relation to mechanisms found in other neural models. We conclude that any neural model that can accommodate slow activation dynamics can solve the timing task.