Applying differential evolution MCMC to parameterize large-scale spiking neural simulations
Applying differential evolution MCMC to parameterize large-scale spiking neural simulations
复制标题
应用差分进化 MCMC 参数化大规模尖峰神经模拟
DOI：
10.1109/cec.2015.7257081
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
M Yoshida
中科院分区：
文献类型：
--
作者：
R Veale;T Isa;M Yoshida
计算昂贵的大脑正向模型的参数化是一个新的研究领域，由于超级计算资源的缘故，该领域直到最近才变得容易处理。然而，如何在合理的时间内实现对大规模脑模拟的神经、突触和连接参数的准确估计，还缺乏实例说明。提出了一种新的并行差分进化马尔可夫链蒙特卡罗(MCMC)方法MT-Dreamz，用于在短时间内估计复杂尖峰神经元电路的参数。这些参数被估计，以便神经模拟与从中脑视觉/注意区域收集的经验数据相匹配，该区域被称为上丘。参数扫描的结果揭示了参数空间中符合经验数据的几个区域。最高似然参数区域显示出与原始大脑区域的解剖属性一致的规律，例如宽广的水平抑制神经元。我们的结果表明，进化统计技术由于其效率和并行化能力，是研究复杂大脑模型的高效工具。扫描不仅为一个极其复杂的非线性问题找到了很好的拟合，而且由此产生的后验似然分布显示了符合从同一大脑区域的解剖学和生理学研究中独立获得的数据的模式，这些数据没有包括在适应度函数中。这表明，仅使用合理数量的行为数据，混合进化方法就可以应用于增加我们对大脑潜在结构和动态属性的理解。
Parameterization of computationally expensive forward models of the brain is a novel research area that has only recently become tractable due to supercomputing resources. However, there is a lack of examples demonstrating how to achieve accurate estimates of neural, synaptic, and connectivity parameters of large-scale brain simulations within a reasonable period of time. We present the novel application of MT-DREAMZ, an existing parallel differential evolution Markov chain monte carlo (MCMC) method, to estimate the parameters of a complex spiking neuron circuit simulation in a short period of time. The parameters are estimated so that the neural simulations match empirical data collected from a midbrain visual/attention region called the superior colliculus. The results of the parameter sweeps reveal several regions of parameter space that fit the empirical data. The highest likelihood parameter regions show regularities consistent with anatomical properties of the original brain region, such as the wide horizontal inhibitory neurons. Our results demonstrate that evolutionary statistical techniques are highly effective tools for investigating complex models of the brain due to their efficiency and parallelizability. Not only do the sweeps find good fits for an incredibly complex non-linear problem, but the resulting posterior likelihood distributions show patterns that fit independently obtained data from anatomical and physiological studies of the same brain region that were not included in the fitness function. This demonstrates that hybrid evolutionary methods can be applied to increase our understanding of the underlying structural and dynamic properties of the brain using only reasonable amounts of behavioral data.