Formulating a Cognitive Branching Task by MTRNN: A Robotic Neuroscience Experiments to Simulate the PFC and Its Neighboring Regions

Abstract

The foremost objective of our research series is to construct a neurocomputational model that aims to achieve a Large-Scale Brain Network (LSBN), and to offer a better insight of how the macro-level anatomical structures, such as the connectivity between the frontal lobe regions and their dynamic properties, can be self-organized to obtain the higher order cognitive mechanisms. To address this issue, this paper focuses in proposing a model that intends to understand the mechanisms underlying the cognitive branching function, a higher order cognitive mechanism, in which a delaying to the execution of an original task occurs until the completion of a subordinate task. The model is constructed by a hierarchical Multi-Timescale Recurrent Neural Network (MTRNN) and conducted on a humanoid robot in a physical environment. Experimental results suggest possible neural activities and network’s layout at the investigated regions that act as an important factor to accomplish such a task.