一个基于memristor的新生物神经网络电路,具有跨模式集成和遗忘效应
Fan Shi1, Kaihua Wang2, Yinghong Cao1
1School of Information Science and Engineering, Dalian Polytechnic University, Dalian, 116034, China.
概括
这项研究介绍了一种新的大脑启发的人工智能网络,模拟交叉模式的关联记忆. 以认知为驱动的生物交叉模式协同记忆器神经网络 (CAMNN) 模仿了高级AI应用的脑功能.
科学领域:
- 人工智能的人工智能
- 神经科学是一个神经科学.
- 电气工程 电气工程
背景情况:
- 大脑的认知功能依赖于学习和记忆之间的交叉模式相互作用.
- 视觉嗅觉关联记忆为理解这些大脑机制提供了一个模型.
研究的目的:
- 提出一种由认知驱动的生物交叉模式协同记忆器神经网络 (CAMNN).
- 模拟大脑中学习,记忆和遗忘的完整过程.
- 为了建模交叉模式的关联记忆相互作用.
主要方法:
- 设计了一个带有电压控制,突触 (基于memristor) 和延迟模块的电路.
- 实现传感信息输入,集成和长期响应维护.
- 利用memristors模拟协同记忆的突触重量.
主要成果:
- 该CAMNN实现了包括值检测,单模学习,交叉模式学习和交叉模式强化学习在内的功能.
- PSPICE模拟证实了电路模拟跨模态相互作用的能力.
- 证明了网络模仿大脑记忆过程的能力.
结论:
- 拟议的CAMNN有效地模拟大脑启发的交叉模式关联记忆.
- 这项工作为开发智能机器人和神经网络电路提供了一个参考.
- 突出了基于memristor的网络对AI的潜力.
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