Static and dynamic memory to simulate higher-order cognitive tasks

The foremost objective of our research series is to construct a neurocomputational model that aims to achieve a Large-Scale Brain Network, and to suggest a possible 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, such as: planning, reasoning, task switching, cognitive branching, etc. For addressing these issues, this paper, in particular, focuses in proposing a model that intends to clarify the neural structure and mechanisms underlying the task switching and the cognitive branching condition. Although both tasks requiring varying degree of a working memory, in contrast to the switching task, where the primary ongoing task is entirely replaced by a new task, in the branching task, a delaying to the execution of an original task occurs until the completion of a subordinate task. The proposed model is constructed by a hierarchical Multiple Timescale Recurrent Neural Network (MTRNN) and conducted on a humanoid robot in a physical environment. Experimental results suggest essential factors related to the neural activities and network's structure necessary to form a suitable working memory for accomplishing such tasks.