弱对对相关性意味着神经群体中强烈相关的网络状态
Elad Schneidman1, Michael J Berry, Ronen Segev
1Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA. elads@princeton.edu
Nature
|April 21, 2006
概括
复杂的神经网络表现出集体行为,即使神经元对应关系较弱. 最大模型解释了这一点,表明神经代码中的关联性质.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 复杂的系统复杂的系统.
背景情况:
- 由于大量可能的状态,分析生物网络具有挑战性.
- 简化假设至关重要,但在大群体中越来越多地认识到更高层次的相互作用.
- 了解神经网络动态需要考虑复杂的相互依存关系.
研究的目的:
- 研究神经网络中对对相关性和集体行为之间的关系.
- 确定基于对对相关性的简单模型是否可以解释复杂的网络活动.
- 探索神经代码的影响,例如关联性或纠错性质.
主要方法:
- 研究脊椎动物视网膜中的神经活动,专注于10个或更多神经元的组.
- 使用最大模型,相当于Ising模型,来分析网络行为.
- 评估了双对的神经元相关性和集体反应模式.
主要成果:
- 在大神经群体中,弱对神经元相关性与强烈集体行为共存.
- 最大模型,只使用对对相关性,量化描述了观察到的集体行为.
- 这些模型预测,相关性效应在更大的网络中占主导地位.
结论:
- 神经网络中的集体行为可以从双对相互作用中出现,正如最大模型所描述的那样.
- 这些发现表明神经代码可能具有关联性或纠错能力.
- 在培养的皮层神经元网络中观察到类似的集体动态,这表明更广泛的适用性.
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