Facilitating neural dynamics for delay compensation: a road to predictive neural dynamics?
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abstract
Goal-directed behavior is a hallmark of cognition. An important prerequisite to goal-directed behavior is that of prediction. In order to establish a goal and devise a plan, one needs to see into the future and predict possible future events. Our earlier work has suggested that compensation mechanisms for neuronal transmission delay may have led to a preliminary form of prediction. In that work, facilitating neuronal dynamics was found to be effective in overcoming delay (the Facilitating Activation Network model, or FAN). The extrapolative property of the delay compensation mechanism can be considered as prediction for incoming signals (predicting the present based on the past). The previous FAN model turns out to have a limitation especially when longer delay needs to be compensated, which requires higher facilitation rates than FAN's normal range. We derived an improved facilitating dynamics at the neuronal level to overcome this limitation. In this paper, we tested our proposed approach in controllers for 2D pole balancing, where the new approach was shown to perform better than the previous FAN model. Next, we investigated the differential utilization of facilitating dynamics in sensory vs. motor neurons and found that motor neurons utilize the facilitating dynamics more than the sensory neurons. These findings are expected to help us better understand the role of facilitating dynamics in delay compensation, and its potential development into prediction, a necessary condition for goal-directed behavior.
