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.

翻译：理解大脑如何通过生成的神经信号与外部世界交互，对于揭示其工作机制、治疗脑疾病以及理解智能本质至关重要。尽管已有诸多理论模型被提出，但迄今仍难以整合与发展。本研究受网格细胞启发，构建了更通用且稳健的网格模块，并将其与贝叶斯推理相结合，形成一种交互式自强化认知系统——即"空间划分与探索-利用"的网格反馈方法（Grid-SD2E）。其中，网格模块既可充当外部世界与系统间的交互媒介，亦可作为系统内部的自我强化媒介；空间划分与探索-利用（SD2E）模块通过其空间划分（SD）组件接收网格的0/1信号。本文所述系统是基于其他研究者的实验及我们在神经解码领域的经验推导出的理论模型。我们依据神经科学与认知科学现有理论分析了系统的合理性，并尝试提出特殊规律与通用规则，用以解释人与人之间、人与外部世界之间的不同交互机制。此外，基于该模型，我们提取了最小计算单元，其功能类似于大脑中的单个神经元。