基于金属有机框架的多种生物功能的突触晶体管与尖端神经网络的LIF模型相结合,以识别时间信息
Qinan Wang1,2, Chun Zhao1, Yi Sun1,2
1School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123 P.R. China.
Microsystems & nanoengineering
|July 24, 2023
概括
泽奥利特式伊米达酸框架创建突触晶体管,模仿神经网络 (SNN) 的大脑功能. 这些SNN在识别电脑电图 (EEG) 频率方面达到95.1%的准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
背景情况:
- 尖端神经网络 (SNN) 提供了低功耗和时间相关处理的潜力.
- 漏洞集成和发射 (LIF) 模型和尖端时间依赖的可塑性 (STDP) 是SNN的核心组件.
- 在神经设备应用中探索了它们的潜力.
研究的目的:
- 用ZIF作为SNN模拟的突触晶体管来演示一个神经设备.
- 模拟关键的神经元功能,包括记忆,突触可塑性和膜潜能动力学.
- 将SNN与非常大规模集成 (VLSI) 集成,用于信号处理和模式识别.
主要方法:
- 使用ZIF制造突触晶体管.
- 短期记忆/长期记忆 (STM/LTM) 和长期抑郁/长期强化 (LTD/LTP) 的建模.
- 从实验数据中提取和安装STDP更新规则.
- 在VLSI中实现LIF模型用于信号转换.
- 从使用LIF过器的EEG记录稳定状态视觉唤起潜力 (SSVEPs).
主要成果:
- 使用基于ZIF的突触晶体管成功模拟了记忆,突触重量调节和膜电位动态.
- 开发基于STDP的重量更新规则.
- 为了高效的信号处理,VLSI实现了LIF.
- 使用SNN识别40种不同的EEG频率的高精度 (95.1%).
- 展示类似大脑的芯片能力.
结论:
- 基于ZIF的突触晶体管有效模拟关键的SNN功能.
- 开发的SNN显示了EEG频率识别的高精度.
- 这项工作推进了类似大脑的计算和人工智能系统.
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