不同数量的离散记忆性突触结合神经元的动态行为效应
Minyuan Cheng1, Kaihua Wang2, Xianying Xu1
1School of Information Science and Engineering, Dalian polytechnic University, Dalian, 116034 China.
Cognitive neurodynamics
|December 23, 2024
概括
这项研究介绍了单独和双离散的记忆性突触神经元模型. 增加的memristors改变系统动态从周期性的混乱,揭示复杂的行为,如费根姆树.
科学领域:
- 计算神经科学是一种神经科学.
- 复杂系统动力学 复杂系统动力学
- 记忆器应用程序 应用程序
背景情况:
- 神经元模型对于理解神经计算和大脑功能至关重要.
- 记忆器为模拟突触可塑性和复杂的神经动态提供了独特的特性.
- 研究离散的记忆性突触神经元模型可以揭示新的计算能力.
研究的目的:
- 构建和分析单一和双重离散记忆型突触神经元模型.
- 调查memristor数量,合强度和放大系数对系统动态的影响.
- 探索复杂的动态行为的产生,包括混沌和费根姆树.
主要方法:
- 单独和双离散记忆性突触神经元模型的开发.
- 使用合强度和放大系数分析平衡点和动态行为.
- 使用NIST SP800-22和DSP硬件实验验证伪随机性的验证.
主要成果:
- 两种模型都有一个不稳定的平衡点.
- 从一个增加到两个的memristors显著改变了系统动态,从周期性/混乱转向纯粹的混乱.
- 一个重聚的费根姆树现象观察到两个memristors,其中参数相互控制动态.
结论:
- 离散的记忆性突触神经元模型表现出丰富且可控制的动态行为.
- 记忆器的数量极大地影响了系统的复杂性和新出现的现象.
- 这些模型为真实神经元的工作原理和潜在的神经形态应用提供了宝贵的见解.
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