与局部活跃的memristor相结合的Hindmarsh-Rose神经元的集体动态
Sathiyadevi Kanagaraj1, Premraj Durairaj1, Sivaperumal Sampath2
1Centre for Nonlinear Systems, Chennai Institute of Technology, Chennai, India.
Bio Systems
|August 26, 2023
概括
局部活跃的memristors模仿结合的印度沼泽-罗斯神经元中的神经突触. 增加的记忆性合强度导致各种网络连接的同步,揭示周期性和混乱状态之间的过渡.
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
- 材料科学 材料科学 材料科学
背景情况:
- 由于其独特的电阻切换特性,memristors为模拟生物突触提供了一个有希望的途径.
- 欣德马什-罗斯神经元模型是一个成熟的数学框架,用于模拟生物神经元的尖端动态.
- 了解神经形态学动力学对于推进大脑启发的计算和人工智能至关重要.
研究的目的:
- 为了研究局部活跃的记忆突触对结合的Hindmarsh-Rose神经元的动态的影响.
- 为了探索周期性和混乱的发射模式之间的过渡的参数空间.
- 检查集体行为,包括同步,在网络的memristive结合的神经元与不同的连接性.
主要方法:
- 使用分叉分析来识别动态过渡.
- 计算了利亚普诺夫指数,以区分周期性和混乱状态.
- 进行了双参数分析,绘制出周期性和混乱动态的区域.
- 进行了网络模拟,使用不同的记忆性合强度和网络拓 (常规,随机,小世界).
主要成果:
- 从周期性到混乱的神经元发射的过渡取决于输入电流和记忆性合强度.
- 分析揭示了系统参数空间内的周期性和混乱行为的不同区域.
- 增加的记忆性合强度诱导了所有检查的网络连接的所有同步.
- 在正规,随机和小世界网络结构中观察到同步.
结论:
- 局部活跃的记忆突触可以有效调节Hindmarsh-Rose神经元的动态,从而导致复杂的神经形态行为.
- 记忆性合强度是控制过渡到混乱并实现网络同步的关键参数.
- 记忆性合的Hindmarsh-Rose神经元网络表现出强大的同步能力,无论它们的底层连接模式如何,突出了基于memristor的神经形态系统的潜力.
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