Comprehending how the brain interacts with the external world through
generated neural signals 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 model, the smallest
computing unit is extracted, which is analogous to a single neuron in the
brain.

探讨大脑如何通过产生神经信号与外界进行交互的工作机制，治疗大脑疾病，理解智力的重要性；使用网格细胞创建更普遍和强大的网格模块 SD2E，构建一种交互和自我强化的认知系统；可以用网格模块作为外界和系统之间的交互介质以及系统内的自我强化介质；通过分析现有的神经科学和认知科学理论，提出特殊和通用规则来解释人与外部世界之间的不同互动。