神经元作为一个直接的数据驱动控制器
Jason J Moore1,2, Alexander Genkin2, Magnus Tournoy2
1Neuroscience Institute, New York University Grossman School of Medicine, New York City, NY 10016.
概括
神经元充当最佳反控制器,引导它们的环境向所需状态. 这种新模型解释了复杂的神经行为,如可塑性转移和运行可变性,超越了传统的神经元模型.
科学领域:
- 计算神经科学是一种计算神经科学.
- 神经生物学 神经生物学 神经生物学
- 控制理论 控制理论 控制理论
背景情况:
- 现有的规范模型主要侧重于预测.
- 生理学数据中的差距需要新的方法来建模神经元功能.
- 传统的神经元模型 (例如,麦卡洛赫-皮茨-罗森布拉特) 的范围有限.
研究的目的:
- 将神经元概念化为最佳的反控制器.
- 扩展神经元功能的规范理论超出预测范围.
- 开发一种神经元控制的生物知情模型.
主要方法:
- 使用直接数据驱动控制 (DD-DC) 框架.
- 模拟神经元作为控制器,将它们的环境引导到所需状态.
- 结合突触反来评估控制的有效性.
主要成果:
- 该DD-DC神经元模型解释了尖端时间依赖的可塑性 (潜能-抑郁转移).
- 该模型考虑了神经元过器的持续时间和适应性.
- 它阐明了峰值生成的不精确性和大脑的操作变化/噪音.
结论:
- 神经元作为最佳的反控制器,而不仅仅是预测器.
- DD-DC框架为神经计算提供了一个生物学上可信的模型.
- 这种方法为神经网络构建提供了一个基本的,现代的单元.
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