同步多元体的稳定性及其非线性行为在memristive合离散神经元模型中
Dianavinnarasi Joseph1, Suresh Kumarasamy2, Sayooj Aby Jose3,4
1Center for Computational Biology, Easwari Engineering College, Chennai, Tamilnadu 600089 India.
Cognitive neurodynamics
|December 23, 2024
概括
这项研究揭示了与memristor合的离散FitzHugh-Nagumo神经元模型中控制同步稳定的功率定律和线性关系. 这些发现有助于预测稳定和不稳定同步模式之间的过渡.
科学领域:
- 计算神经科学是一种神经科学.
- 非线性动力学是一种非线性动力学.
- 复杂的系统复杂的系统.
背景情况:
- 离散的菲茨休-纳古莫 (DFHN) 模型是神经元动态的简化表示.
- 记忆器合引入了神经网络中复杂的,依赖状态的相互作用.
- 了解同步稳定对于神经网络功能和大脑启发的计算至关重要.
研究的目的:
- 调查第一和第二阶段合强度对同步管稳定性的影响,用memristor合的DFHN模型.
- 识别和描述控制稳定性过渡的关系.
- 为设计更高效的神经网络提供见解.
主要方法:
- 利用主稳定性函数 (MSF) 来分析同步分流器的稳定性.
- 不同的第一级和第二级合强度和流量系数.
- 分析了DFHN模型的零十字路口和稳定性制度的MSF.
主要成果:
- 多元框架展示了两个零交叉点,表明了不同的稳定性制度.
- 零交叉遵循与合强度和流量系数的功率规律关系.
- 零交叉显示了第一级和第二级合强度之间的线性关系.
- 较高的合强度导致指数级缩小的稳定同步模式.
结论:
- 在memristor合的DFHN神经元中确定了同步稳定性的可预测关系 (线性和功率定律).
- 开发了一种基于合参数的稳定性转换预测方法.
- 证明更高的合强度降低了稳定的同步多重体,这对大脑启发的计算和神经网络效率有影响.
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