走向一种生物学上可信的基于SNN的关联记忆,具有上下文依赖的Hebbian连接性
S Yu Makovkin1, S Yu Gordleeva2,3,4, I A Kastalskiy5,6
1Department of Applied Mathematics, Institute of Information Technology, Mathematics and Mechanics, Lobachevsky State University of Nizhny Novgorod, 23 Gagarin Avenue, Nizhny Novgorod 603022, Russia.
International journal of neural systems
|April 20, 2025
概括
我们使用Hebbian学习开发了一种节能尖端神经网络,用于协会记忆. 这个模型使用同步的神经元振荡来识别二进制图像,为先进的AI硬件铺平了道路.
科学领域:
- 计算神经科学是一种神经科学.
- 人工智能的人工智能
- 神经计算是一种神经计算.
背景情况:
- 关联记忆对于认知功能至关重要.
- 现有的模型往往缺乏能源效率.
- 尖端神经网络提供了一个生物学上可信和潜在的高效替代方案.
研究的目的:
- 为节能协同记忆提出一种新的尖端神经网络模型.
- 实现Hebbian学习以进行信息存储和检索.
- 探索用于模式识别的上下文依赖信号处理.
主要方法:
- 一个使用霍奇金-哈克斯利-梅宁神经元的三层尖端神经网络.
- 通过对称连接矩阵实现的Hebbian学习.
- 使用相位/反相位振荡和相位锁定进行同步的二进制图像编码.
- 内神经元用于对突触通路的上下文依赖过.
主要成果:
- 通过刺激反应证明了信息模式的检索.
- 通过相锁实现了输入和输出层中的集群同步.
- 展示了用于识别的突触连接的上下文依赖的参与.
- 研究了用于直接和反向图像识别的振荡相位稳定性.
结论:
- 拟议的模型提供了对关联记忆的节能方法.
- 情境依赖处理增强了识别能力.
- 该模型显示了在神经计算和人工智能中模拟硬件实现的潜力.
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