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Inference of a Mesoscopic Population Model from Population Spike Trains
Neural Computation ( IF 2.9 ) Pub Date : 2020-08-01 , DOI: 10.1162/neco_a_01292
Alexandre René 1 , André Longtin 2 , Jakob H Macke 3
Affiliation  

Understanding how rich dynamics emerge in neural populations requires models exhibiting a wide range of behaviors while remaining interpretable in terms of connectivity and single-neuron dynamics. However, it has been challenging to fit such mechanistic spiking networks at the single-neuron scale to empirical population data. To close this gap, we propose to fit such data at a mesoscale, using a mechanistic but low-dimensional and, hence, statistically tractable model. The mesoscopic representation is obtained by approximating a population of neurons as multiple homogeneous pools of neurons and modeling the dynamics of the aggregate population activity within each pool. We derive the likelihood of both single-neuron and connectivity parameters given this activity, which can then be used to optimize parameters by gradient ascent on the log likelihood or perform Bayesian inference using Markov chain Monte Carlo (MCMC) sampling. We illustrate this approach using a model of generalized integrate-and-fire neurons for which mesoscopic dynamics have been previously derived and show that both single-neuron and connectivity parameters can be recovered from simulated data. In particular, our inference method extracts posterior correlations between model parameters, which define parameter subsets able to reproduce the data. We compute the Bayesian posterior for combinations of parameters using MCMC sampling and investigate how the approximations inherent in a mesoscopic population model affect the accuracy of the inferred single-neuron parameters.

中文翻译:

从人口峰值序列推断介观人口模型

了解丰富的动态如何在神经群体中出现需要模型表现出广泛的行为,同时在连接性和单神经元动态方面保持可解释性。然而,将这种单神经元规模的机械尖峰网络与经验人口数据相适应一直具有挑战性。为了缩小这一差距,我们建议使用机械但低维的模型在中尺度上拟合这些数据,因此,在统计上易于处理。通过将一组神经元近似为多个同质的神经元池,并对每个池内的聚合种群活动的动态进行建模,可以获得细观表示。鉴于此活动,我们推导出单神经元和连接参数的可能性,然后可用于通过对数似然梯度上升来优化参数或使用马尔可夫链蒙特卡罗 (MCMC) 采样执行贝叶斯推理。我们使用之前已经推导出细观动力学的广义积分和激发神经元模型来说明这种方法,并表明可以从模拟数据中恢复单神经元和连接参数。特别是,我们的推理方法提取了模型参数之间的后验相关性,这些参数定义了能够重现数据的参数子集。我们使用 MCMC 采样计算参数组合的贝叶斯后验,并研究介观总体模型中固有的近似值如何影响推断的单神经元参数的准确性。
更新日期:2020-08-01
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