A Derived Functional Framework for Cognitive Computational Learning System

The motivations of this work are the comprehensive construction and computational analysis of a model structure derived objectively from experimental findings in brain neural data, including the observation that an unsupervised decoded trajectory exhibits a degree of symmetry with the actual trajectory within the activity space. In this study, we are also inspired by the formation of grid cells to create a more general and robust grid module and to construct an interactive, self-reinforcing learning system that incorporates Bayesian inference; the proposed approach is interpreted as spatial division and exploration-exploitation with grid feedback, abbreviviated as Grid-SD2E. Here, the grid module can be used as an interaction medium between the outside world and a system, as well as a self-reinforcement medium within the system. The spatial division and exploration-exploitation (SD2E) mechanism receives the 0/1 signals of a grid through its spatial division (SD) module. Herein, we analyse the rationality of the target system on the basis of existing theories in both neuroscience and cognitive science and propose a general learning principle (i.e., special and general rules) to explain the different interactions between people and between people and the external world. Finally, we believe that the Grid-SD2E framework should be regarded as a computational model of the brain (essentially a cognitive learning system). However, it should be noted that since the model structure is derived akin to a geometric drawing, making its scientific implications difficult to understand, we attempt to interpret this model structure from multiple perspectives and disciplines rather than venturing into other fields.