Identification of Connectivity in Neural Networks.

机译：神经网络中连通性的识别。

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摘要

Analytical and experimental methods are provided for estimating synaptic connectivities from simultaneous recordings of multiple neurons. The results are based on detailed, yet flexible neuron models in which spike trains are modeled as general doubly stochastic point processes. The expressions derived can be used with non-stationary or stationary records, and can be readily extended from pair-wise to multi-neuron estimates. Furthermore, we show analytically how the estimates are improved as more neurons are sampled, and derive the appropriate normalizations to eliminate stimulus-related correlations. Finally, we illustrate the use and interpretation of the analytical expressions on simulated spike trains and neural networks, and give explicit confidence measures on the estimates.

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作者
Yang, X.; Shamma, S. A.;
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年度 1989
页码 1-41
总页数 41
原文格式 PDF
正文语种 eng
中图分类工业技术;
关键词
Neural nets; Simulation; Stochastic processes; Mathematical analysis; Recording systems; Synchronism; Confidence level; Synaptic connectivities;

机译：神经网络;模拟;随机过程;数学分析;记录系统;同步;置信水平;突触连通性;

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Identification of Connectivity in Neural Networks.

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