  Comprehending how the brain interacts with the external world through
generated neural data is crucial for determining its working mechanism,
treating brain diseases, and understanding intelligence. Although many
theoretical models have been proposed, they have thus far been difficult to
integrate and develop. In this study, we were inspired in part by grid cells in
creating a more general and robust grid module and constructing an interactive
and self-reinforcing cognitive system together with Bayesian reasoning, an
approach called space-division and exploration-exploitation with grid-feedback
(Grid-SD2E). Here, a 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 space-division and exploration-exploitation (SD2E) receives the
0/1 signals of a grid through its space-division (SD) module. The system
described in this paper is also a theoretical model derived from experiments
conducted by other researchers and our experience on neural decoding. Herein,
we analyse the rationality of the system based on the existing theories in both
neuroscience and cognitive science, and attempt to propose special and general
rules to explain the different interactions between people and between people
and the external world. What's more, based on this framework, the smallest
computing unit is extracted, which is analogous to a single neuron in the
brain.
