从连接体到效应体:学习大脑的因果相互作用地图
Dean A Pospisil1, Max J Aragon1, Sven Dorkenwald1,2
1Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
bioRxiv : the preprint server for biology
|November 14, 2023
概括
科学家们开发了一种新的方法来绘制大脑的"效能组",揭示神经回路是如何导致行为的. 这种方法有效地模拟了大脑动态,为神经系统的因果模型铺平了道路.
科学领域:
- 神经科学是一个神经科学.
- 计算神经科学是一种神经科学.
- 系统神经科学 系统神经科学
背景情况:
- 了解神经系统需要一个因果模型,将神经动态与行为联系起来.
- 大脑连接组映射神经连接,但缺乏关于它们在体内因果作用的信息.
- 现有的方法很难仅仅从解剖学数据中推断出因果关系.
研究的目的:
- 开发一种新的策略,有效地学习一种叫做"效因组"的大脑因果模型.
- 通过实验和统计方法,估计大脑中神经元相互作用的因果关系.
- 为了确定驱动神经系统动态的关键神经电路.
主要方法:
- 提出了一种飞大脑动态系统模型的估计器.
- 利用随机光遗传扰动数据来估计因果关系.
- 整合了大脑连接组作为一个优先级来提高估计效率.
主要成果:
- 确定了对脑动态有显著影响的主导神经回路,涉及小的神经元群体.
- 证明该方法重新发现已知的电路并产生新的,可测试的假设.
- 发现证据表明,全球大脑动力学源于基本上独立的局部电路.
结论:
- 使用开发的"效组"方法,可以实现大脑的因果模型.
- 大脑的全球动态是由许多小的,在很大程度上独立的电路编排的.
- 这项工作将系统神经科学推进到实现大脑功能的因果模型.
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