使用 SilentMLE Python 包从经过模拟器校正的电生理数据中准确估计静默突触。
Accurate Silent Synapse Estimation from Simulator-Corrected Electrophysiological Data Using the SilentMLE Python Package.
机构信息
Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
Centre for Neural Dynamics, University of Ottawa, Ottawa, ON K1H 8M5, Canada.
出版信息
STAR Protoc. 2020 Nov 24;1(3):100176. doi: 10.1016/j.xpro.2020.100176. eCollection 2020 Dec 18.
The proportion of silent (AMPAR-lacking) synapses is thought to be related to the plasticity potential of neural networks. We created a maximum-likelihood estimator of silent synapse fraction based on simulations of the underlying experimental methodology. Here, we provide a set of guidelines for running a Python package on compatible experimental synaptic data. Compared with traditional failure-rate approaches, this synthetic likelihood estimator improves the validity and accuracy of the estimates of the silent synapse fraction. For complete details on the use and execution of this protocol, please refer to Lynn et al. (2020).
据认为，沉默（AMPA 受体缺乏）突触的比例与神经网络的可塑性潜力有关。我们根据潜在实验方法学的模拟，创建了沉默突触分数的最大似然估计器。在这里，我们提供了一组在兼容的实验突触数据上运行 Python 包的指南。与传统的故障率方法相比，这种合成似然估计器提高了沉默突触分数估计的有效性和准确性。有关此协议的使用和执行的完整详细信息，请参阅 Lynn 等人。（2020 年）。
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