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首页> 外文期刊>Journal of Computational Neuroscience >Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings
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Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings

机译：从峰值记录快速推断随机整合和发射神经元集合中的​​相互作用

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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 5 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.
机译：我们提出了两种贝叶斯程序，从它们的尖峰活动记录中推断出随机整合和发射神经元集合中的​​相互作用和外部电流。第一个过程基于对由记录的尖峰调节的神经元膜电位最可能的时程的精确计算，并且对于消失的噪声方差和瞬时突触整合是精确的。第二种方法考虑到了最有可能的电位时间过程周围的波动，并可以处理中等噪声水平。这两个过程的运行时间与峰值数5乘以神经元的平方数N成正比。在具有已知耦合和电流的网络生成的合成数据上对算法进行了验证。我们还重新分析了published视网膜的活动（包括32至40个神经元，以及65,000至170,000个尖峰）的先前记录。我们研究推断的相互作用对膜泄漏时间的依赖性。讨论了与经典互相关分析的异同。

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