An Approach for the Binding Problem Based on Brain-oriented Autonomous Adaptation System with Object Handling Functions

An approach for the binding problem is proposed, based on an autonomous adaptive system designed using artificial neural networks with object handling functions. Object handling functionality, such as object files, has been reported to have a relationship with perception, and working memory. However, in order for a brain-oriented system to decide actions based on object handling, the system must clarify the “binding problem”, or the problem of processing different attributes such as shape, color and location in parallel, then binding these multiple attributes as a single object. The proposed system decides semi-optimum actions by combining nonlinear programming and reinforced learning. By the introduction of artificial neural networks based on dendritic structures of pyramidal neurons in the cerebral cortex, together with a mechanism for dynamically linking nodes to objects, it is shown that deciding actions and learning as a whole system, based on binding object attributes and location, is possible. The proposed features are verified through computer simulation results.