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。

Grid-SD2E: 一种认知学习系统中的通用网格反馈

Grid-SD2E: A General Grid-Feedback in a System for Cognitive Learning

At this moment, databanks worldwide contain brain images of previously
unimaginable numbers. Combined with developments in data science, these massive
data provide the potential to better understand the genetic underpinnings of
brain diseases. However, different datasets, which are stored at different
institutions, cannot always be shared directly due to privacy and legal
concerns, thus limiting the full exploitation of big data in the study of brain
disorders. Here we propose a federated learning framework for securely
accessing and meta-analyzing any biomedical data without sharing individual
information. We illustrate our framework by investigating brain structural
relationships across diseases and clinical cohorts. The framework is first
tested on synthetic data and then applied to multi-centric, multi-database
studies including ADNI, PPMI, MIRIAD and UK Biobank, showing the potential of
the approach for further applications in distributed analysis of multi-centric
cohorts

提出了一种联邦学习框架，可以安全地访问和元分析任何医学数据，探究神经系统疾病的关系。该框架在合成数据和多中心和多数据库的研究中进行了测试，表明了在分布式分析多中心队列中的进一步应用的潜力。