基于模拟推断简单神经网络结构：用脉冲序列统计量估计连接概率
Simulation Based Inference of a Simple Neural Network Structure
依据摘要，作者提出不依赖互相关等关联统计量，而改用经验脉冲频率、脉冲间隔分布等简单脉冲序列统计量，通过模拟估计其抽样分布，进而推断所记录神经元网络的连接概率。在“玩具模型”上，该方法对原始网络连接概率的推断结果显著优于子网络重建方法。研究针对的是细胞外电极记录只能覆盖神经元群体极小部分这一欠采样问题，目前结果来自模拟模型，能否用于真实记录数据仍需验证；该预印本尚未完成同行评审。
arXiv:2604.18599v2 Announce Type: replace-cross Abstract: Neurophysiologists are nowadays able to record from a large number of extracellular electrodes and to extract, from the raw data, the sequences of action potentials or spikes generated by many neurons. Unfortunately these ''many neurons'' still represent only a tiny fraction of the neuronal population that constitutes the network. Using association statistics such as the estimation of the cross-correlation functions, they are trying to infer the structure of the network formed by the recorded neurons. But this inference is compromised by the tremendous under-sampling of the neuronal population. We propose to focus instead on simple spike train statistics, like the empirical spikes frequency, or the interspike interval distribution. Their sampling distributions can be estimated by simulations, and, given a few observed spike train statistics, they provide enough information to infer the structure of the underlying network. We show that, on a ''toy model'', our method gives significantly better results than the sub-network reconstruction method with regards to the inference of the connection probability of the original network.
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来源：arXiv q-bio.NC 预印本 · arxiv.org