在双终端的memristor中调整Bienenstock-Cooper-Munro学习规则,用于神经形态计算
Zeyang Li1, Peilin Liu1, Guanghong Yang2
1School of Future Technology, Henan Key Laboratory of Photovoltaic Materials, Henan University, Kaifeng 475004, China.
Physical chemistry chemical physics : PCCP
|June 1, 2023
概括
这项研究通过调整连续电阻,在双终端的memristor中展示了三种Bienenstock-Cooper-Munro (BCM) 学习规则. 这些发现推动了神经形态计算的人工突触调制和能源效率.
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:
- 贝恩斯托克-库珀-蒙罗 (BCM) 学习规则对于神经形态计算中的人工突触至关重要,它可以增强突触调制平衡并减少能量消耗.
- 目前的BCM规则的实现通常依赖于复杂的场效应晶体管,由于调节参数不足,限制了更简单的双终端记忆器的进展.
研究的目的:
- 通过操纵连续电阻,研究在双终端的memristor中实现BCM类学习规则的可行性.
- 探索不同类型的BCM类可塑性及其对设备参数的依赖.
主要方法:
- 使用了一个双终端 BaTiO3 记忆器装置.
- 系统地调整序列电阻以观察突触可塑性的变化.
- 采用X射线光电子光谱 (XPS) 来分析潜在的物理机制.
主要成果:
- 通过不同的系列阻力确定了三种不同的BCM类学习规则:异常 (低阻力),单调 (高阻力) 和增强抑郁 (中等阻力).
- 证明这些规则与由电离氧空位迁移驱动的非单调导电量调制有关.
- 实现了尖峰速度依赖的可塑性 (SRDP) 和历史依赖的可塑性.
结论:
- 通过控制连续电阻,成功地在一个简单的双终端memristor中实现了各种BCM类的学习规则.
- 这些发现为开发节能的人工突触和推进神经形态计算硬件提供了新的途径.
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