分数顺序的memcapacitive神经形态元素可以复制和预测神经元功能.
Patricia Vazquez-Guerrero1, Rohisha Tuladhar1, Costas Psychalinos2
1Department of Neuroscience, Developmental and Regenerative Biology, The University of Texas at San Antonio, San Antonio, TX, 78349, USA.
Scientific reports
|March 9, 2024
概括
使用超级电容器实现的分数顺序的mem电容器使得具有历史依赖的神经功能的高效神经形态系统成为可能. 这些mem电容器模仿生物神经元,显示出强度定律适应性和高级计算的关键性.
科学领域:
- 神经科学是一个神经科学.
- 电气工程 电气工程
- 计算科学 计算科学
背景情况:
- 神经形态系统需要能量和计算效率.
- 电子元件 (mems) 可以本质上实现神经元功能.
- 历史依赖的尖峰时间适应对生物神经元至关重要,但在神经形态系统中未得到充分利用.
研究的目的:
- 通过使用超级电容器来实现分数顺序的神经元激增.
- 调查这些电路中的功率定律峰值时间适应和最佳编码特性.
- 探索分数顺序的mem电容器对于高效的神经形态计算的潜力.
主要方法:
- 使用超级电容器实现了有漏洞的分数顺序整合和火和霍奇金-哈克斯利神经元电路.
- 描述了实施的电路的尖端动态和适应性质.
- 将电路动力学与来自弱电鱼神经元的实验记录进行比较.
主要成果:
- 基于超级电容器的电路表现出功率规律升时间适应和最佳编码.
- 分数顺序的霍奇金-哈克斯利电路展示了与关键性相一致的新动态.
- 电路动力学与活鱼记录进行了验证,证实了临界性并预测了实验结果.
结论:
- 分数顺序的mem电容器为计算效率高的神经形态设备提供了内在的内存依赖性.
- 记忆电容器为能量高效的神经形态硬件提供了对记忆电源的低能耗替代方案.
- 这项工作将神经元适应的理论模型与实际硬件实现相结合.
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