在亚样本神经网络中,统计学推断的神经连接与尖峰列车协差强烈相关
Tong Liang1,2, Braden A W Brinkman2
1Department of Physics and Astronomy, Stony Brook University, Stony Brook, New York 11794, USA.
Physical review. E
|May 17, 2024
概括
从尖端数据推断的神经连接的推断可能是不准确的,因为未观察到的神经元. 这项研究表明,推断的联系可能反映了非因果关系,而不是真正的解剖联系,特别是在部分观察到的网络中.
科学领域:
- 计算神经科学是一种计算神经科学.
- 神经网络建模神经网络建模
- 在神经科学中的统计推断.
背景情况:
- 从尖峰列车数据中推断神经元连接至关重要,但具有挑战性.
- 尖列车协差 (功能连接) 经常被使用,但是非因果的,不代表解剖连接.
- 最大概率推断可以强制执行因果关系,但仍然可能是有偏见的.
研究的目的:
- 调查在部分观察到的网络中统计推断的神经元连接和尖峰列车协差之间的关系.
- 为了确定推断的连接是否准确地反映了各种条件下的基本真实解剖连接.
- 了解未观察到的神经元和弱合对连接推断的影响.
主要方法:
- 随机泄漏的整合和火网络的模拟.
- 带有波动纠正的平均场分析.
- 推断的突触过器,尖列车共变率和基本真相连接的比较.
主要成果:
- 推断的神经元连接与尖峰列车协差强烈相关,即使使用最大概率推断.
- 这种相关性可以导致推断非因果关系,特别是当许多神经元未被观察到或合较弱时.
- 这种现象在不同的网络结构中观察到,包括随机和平衡的刺激-抑制网络.
结论:
- 从部分观察到的网络中从尖峰数据中推断神经元连接的统计推断可以被非因果关系所混.
- 在处理未观察到的神经元或弱合时,尖列车共变是解剖连接的糟糕代理.
- 需要仔细考虑网络可观测性和合强度,才能准确地推断神经元连接.
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