Recent advancements in artificial intelligence have propelled the capabilities of Large Language Models, yet their ability to mimic nuanced human reasoning remains limited. This paper introduces a novel conceptual enhancement to LLMs, termed the Artificial Neuron, designed to significantly bolster cognitive processing by integrating external memory systems. This enhancement mimics neurobiological processes, facilitating advanced reasoning and learning through a dynamic feedback loop mechanism. We propose a unique framework wherein each LLM interaction specifically in solving complex math word problems and common sense reasoning tasks is recorded and analyzed. Incorrect responses are refined using a higher capacity LLM or human in the loop corrections, and both the query and the enhanced response are stored in a vector database, structured much like neuronal synaptic connections. This Artificial Neuron thus serves as an external memory aid, allowing the LLM to reference past interactions and apply learned reasoning strategies to new problems. Our experimental setup involves training with the GSM8K dataset for initial model response generation, followed by systematic refinements through feedback loops. Subsequent testing demonstrated a significant improvement in accuracy and efficiency, underscoring the potential of external memory systems to advance LLMs beyond current limitations. This approach not only enhances the LLM's problem solving precision but also reduces computational redundancy, paving the way for more sophisticated applications of artificial intelligence in cognitive tasks. This paper details the methodology, implementation, and implications of the Artificial Neuron model, offering a transformative perspective on enhancing machine intelligence.

翻译：近期人工智能的进步推动了大型语言模型能力的提升，但其模拟细微人类推理的能力仍存在局限。本文提出一种称为"人工神经元"的新型概念增强方案，通过整合外部记忆系统显著强化认知处理能力。该增强机制模拟神经生物学过程，借助动态反馈回路实现高阶推理与学习。我们设计了一个独特框架：系统记录并分析每次LLM交互（尤其是解决复杂数学应用题和常识推理任务时的交互）。错误响应通过更高容量LLM或人工循环修正进行优化，查询内容和增强后的响应均被存储于向量数据库中，其结构类似于神经突触连接。该人工神经元作为外部记忆辅助系统，使LLM能够参考过往交互经验并将已习得的推理策略应用于新问题。实验设置采用GSM8K数据集进行初始模型响应生成训练，随后通过反馈循环进行系统性修正。后续测试表明，准确率与效率均获得显著提升，验证了外部记忆系统突破当前LLM局限的潜力。该方法不仅增强了LLM的问题解决精度，还降低了计算冗余性，为人工智能在认知任务中的更复杂应用奠定基础。本文详细阐述了人工神经元模型的方法论、实现过程及其深远意义，为提升机器智能提供了变革性视角。