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:理解大脑如何通过产生的神经信号与外部世界进行交互,对于确定其工作机制、治疗脑部疾病和理解智能至关重要。虽然许多理论模型已被提出,但迄今为止它们很难被集成和发展。在本研究中,我们在一定程度上受到网格细胞的启发,创建了一个更通用和强大的网格模块,结合贝叶斯推理构建了一个交互式自我强化的认知系统,这种方法称为带有网格反馈的空间分割和探索开发(Grid-SD2E)。这里,网格模块可以被用作外部世界和系统之间的交互媒介,同时也可以被用作系统内自我强化的媒介。空间分割和探索开发(SD2E)通过其空间分割(SD)模块接收网格的0/1信号。本文所描述的系统模型也是基于其他研究人员的实验和我们在神经解码方面的经验推导出来的。在此,我们根据神经科学和认知科学中的现有理论分析系统的合理性,并试图提出解释人与人之间以及人与外部世界之间不同交互的特殊和一般规则。此外,基于这个模型,我们提取了与大脑中的单个神经元类似的最小计算单元。