从锋电位记录快速推断随机整合-激发神经元集合中的相互作用
Fast Inference of Interactions in Assemblies of Stochastic Integrate-and-Fire Neurons from Spike Recordings
摘要：
我们提出了两个贝叶斯程序来推断的相互作用和外部电流的随机集成和消防神经元的组装从他们的尖峰活动的记录。第一个过程是基于精确计算的最有可能的时间过程的神经元膜电位的条件下记录的尖峰，是精确的消失的噪声方差和瞬时突触整合。第二个过程考虑到周围的电位的最有可能的时间过程中的波动的存在，并可以处理适度的噪声水平。这两个过程的运行时间与尖峰的数量S乘以神经元的平方数N成正比。该算法的验证与已知的耦合和电流的网络所产生的合成数据。我们还重新分析了先前发表的蝾螈视网膜活动记录（包括32至40个神经元和65，000至170，000个尖峰）。我们研究了膜泄漏时间上的相互作用的依赖关系，与经典的互相关分析的差异和相似之处进行了讨论。
英文摘要：
We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely time courses of the neuron membrane potentials conditioned by the recorded spikes, and is exact for a vanishing noise variance and for an instantaneous synaptic integration. The second procedure takes into account the presence of fluctuations around the most likely time courses of the potentials, and can deal with moderate noise levels. The running time of both procedures is proportional to the number S of spikes multiplied by the squared number N of neurons. The algorithms are validated on synthetic data generated by networks with known couplings and currents. We also reanalyze previously published recordings of the activity of the salamander retina (including from 32 to 40 neurons, and from 65,000 to 170,000 spikes). We study the dependence of the inferred interactions on the membrane leaking time; the differences and similarities with the classical cross-correlation analysis are discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
