用多频神经振荡模拟大脑认知，提升AI的灵活性与效率
Neuromorphic Computing with Multi-Frequency Oscillations: A Bio-Inspired Approach to Artificial Intelligence
- 构建感知、辅助、执行三系统协同的类脑架构
- 精度提升2.18%，计算迭代减少48.44%，更贴近人类信心模式
- 为跨认知领域类脑智能提供理论基础，适合研究脑启发AI者
尽管人工神经网络表现卓越，但其智能仍缺乏灵活性和泛化能力。这源于其与生物认知的根本差异——忽视了神经区域的功能特化及协调这些系统的时序动态。本文提出一种三部分类脑架构，包含功能特化的感知、辅助与执行系统。通过模拟多频神经振荡和突触动态适应机制，引入时序动态，显著增强架构性能。初步评估显示，该方法在时序处理任务上优于现有最优方案：准确率提升2.18%，计算迭代次数减少48.44%，且与人类信心模式相关性更高。尽管当前验证于视觉处理任务，该架构为跨认知领域的类脑智能提供了理论基础，有望弥合人工与生物智能间的差距。
原文摘要 · Abstract (English)
Despite remarkable capabilities, artificial neural networks exhibit limited flexible, generalizable intelligence. This limitation stems from their fundamental divergence from biological cognition that overlooks both neural regions' functional specialization and the temporal dynamics critical for coordinating these specialized systems. We propose a tripartite brain-inspired architecture comprising functionally specialized perceptual, auxiliary, and executive systems. Moreover, the integration of temporal dynamics through the simulation of multi-frequency neural oscillation and synaptic dynamic adaptation mechanisms enhances the architecture, thereby enabling more flexible and efficient artificial cognition. Initial evaluations demonstrate superior performance compared to state-of-the-art temporal processing approaches, with 2.18\% accuracy improvements while reducing required computation iterations by 48.44\%, and achieving higher correlation with human confidence patterns. Though currently demonstrated on visual processing tasks, this architecture establishes a theoretical foundation for brain-like intelligence across cognitive domains, potentially bridging the gap between artificial and biological intelligence.
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