工作记忆的双功能状态由基于memristor的神经网络实现
Hongzhe Wang1, Xinqiang Pan1, Junjie Wang1
1State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, China.
Frontiers in neuroscience
|June 23, 2023
概括
这项研究介绍了一种基于memristor的新型神经网络,可以模拟持续的神经发射和活动静音工作记忆. 这种生物学上可信的系统证明了高级神经形态计算的双重功能状态.
科学领域:
- 神经科学是一个神经科学.
- 计算机科学 计算机科学
- 材料科学 材料科学 材料科学
背景情况:
- 工作记忆涉及短期信息存储和操纵.
- 人们认为它依赖于持续的神经元发射和活动静音机制.
- 在生物学上可信的神经形态系统需要模拟两个工作记忆机制.
研究的目的:
- 为工作记忆提出基于memristor的神经网络模型.
- 为了在物理上实现持续的神经发射和活动静音工作记忆.
- 开发一种具有双重功能记忆状态的神经形态系统.
主要方法:
- 设计了一个基于memristor的神经网络,配有人工神经元和突触.
- 实施了获胜者获取全部的学习规则.
- 在认知任务期间,在"集中"和"不集中"工作记忆状态之间演示状态转换.
主要成果:
- 在一个memristor网络中成功实现了持续的神经发射和活动静音工作记忆.
- 展示了内存系统中的双重功能状态.
- 验证了网络执行状态转换的能力.
结论:
- 拟议的基于memristor的神经网络有效模拟关键的工作记忆机制.
- 这种方法为创建生物可信的神经形态计算系统提供了一条途径.
- 进一步的研究可以探索模拟具有独特神经活动的复杂工作记忆功能.
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