基于感知神经网络的生物灵感混沌传感器模型:机器学习概念和计算神经科学的应用
Andrei Velichko1, Petr Boriskov1, Maksim Belyaev1
1Institute of Physics and Technology, Petrozavodsk State University, 33 Lenin str., 185910 Petrozavodsk, Russia.
Sensors (Basel, Switzerland)
|August 26, 2023
概括
这项研究引入了一个生物灵感的混乱传感器,使用感知神经网络来估计神经动力学系统中的尖峰列车. 该模型准确地近似了模糊,有助于计算神经科学和生物机器人的发展.
科学领域:
- 计算神经科学是一种神经科学.
- 生物启发工程 生物启发工程
- 人工智能的人工智能
背景情况:
- 神经动力学系统产生复杂的尖峰列车信号.
- 估计这些信号的对于理解系统动态至关重要.
- 现有的方法可能缺乏准确性或实时分析的效率.
研究的目的:
- 开发一个生物灵感的混沌传感器模型来估计尖峰列车.
- 为了使用感知神经网络来准确地近似.
- 为了证明该模型在神经动力学系统和机器人学中的适用性.
主要方法:
- 训练了一个具有特定隐藏和输出层的感知神经网络.
- 欣德马什-罗斯尖峰模型生成了用于培训和测试的时间序列数据.
- 用K块交叉验证进行过度参数选择和准确性评估.
主要成果:
- 感知子模型在近似模糊中实现了高精度 (R2 ≈ 0.9).
- 即使是使用较少神经元的简化模型也显示出很好的近似度 (R2 ≈ 0.50.8).
- 该模型成功地跟踪了现实世界的动作潜能记录中的混乱行为.
结论:
- 生物启发的混乱传感器有效地估计神经动力学系统中的尖峰列车.
- 该模型为计算神经科学研究提供了一个有前途的工具.
- 它在创建先进的人形,动物和生物机器人方面具有潜在的应用.
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