Yang Yu, Yan Chuankui
College of Mathematics and Physics, Wenzhou University, Wenzhou, 325035 China.
Cogn Neurodyn. 2022 Aug;16(4):961-972. doi: 10.1007/s11571-021-09750-6. Epub 2021 Nov 20.
Revealing synaptic connections between neurons is of great significance and practical value to biomedicine and bio-neurology. We present a general approach to reconstruct neuronal synapses, which is based on compressive sensing and special data processing. And this approach is more suitable for nervous system with peak time series. Numerical simulations illustrate the feasibility and effectiveness of the proposed approach. Moreover, this approach not only adapts to the asymmetry of neural connections and the diversity of coupling strength, but also adapts to the excitability and inhibition of neural node classification. In addition, the effects of the factors on the synaptic connection identification performance and their optimal states for the synaptic connection recovery are discussed. Besides, it is of great practical significance to control the order of Taylor expansion to improve the performance of synaptic connection recognition.
揭示神经元之间的突触连接对生物医学和生物神经学具有重大意义和实用价值。我们提出了一种基于压缩感知和特殊数据处理的重建神经元突触的通用方法。并且这种方法更适用于具有峰值时间序列的神经系统。数值模拟说明了所提方法的可行性和有效性。此外，该方法不仅适应神经连接的不对称性和耦合强度的多样性，还适应神经节点分类的兴奋性和抑制性。另外，讨论了这些因素对突触连接识别性能的影响以及它们用于突触连接恢复的最优状态。此外，控制泰勒展开的阶数对提高突触连接识别性能具有重要的实际意义。
