Automated customization of large-scale spiking network models to neuronal population activity.
Automated customization of large-scale spiking network models to neuronal population activity.
复制标题
根据神经元群体活动自动定制大规模尖峰网络模型。
DOI：
10.1101/2023.09.21.558920
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Yu,Byron
中科院分区：
文献类型：
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作者：
Wu,Shenghao;Huang,Chengcheng;Snyder,Adam;Smith,Matthew;Doiron,Brent;Yu,Byron
通过构建精确再现大脑活动各个方面的计算模型，有助于理解大脑功能。尖峰神经元网络捕捉神经元回路的潜在生物物理学，但它们的活动依赖于模型参数是出了名的复杂。因此，启发式方法被用于配置尖峰网络模型，这可能导致无法发现足够复杂的活动机制来匹配大规模的神经元记录。在这里，我们提出了一个自动过程，使用人口统计（SNOPS）的峰值网络优化，以定制峰值网络模型，再现大规模神经元记录的群体范围的协变性。我们首先证实了SNOPS准确地恢复模拟神经活动统计。然后，我们将SNOPS应用于猕猴视觉和前额叶皮层的记录，发现了以前未知的尖峰网络模型的局限性。综上所述，SNOPS可以指导网络模型的发展，从而能够更深入地了解神经元网络如何产生大脑功能。
Understanding brain function is facilitated by constructing computational models that accurately reproduce aspects of brain activity. Networks of spiking neurons capture the underlying biophysics of neuronal circuits, yet their activity’s dependence on model parameters is notoriously complex. As a result, heuristic methods have been used to configure spiking network models, which can lead to an inability to discover activity regimes complex enough to match large-scale neuronal recordings. Here we propose an automatic procedure, Spiking Network Optimization using Population Statistics (SNOPS), to customize spiking network models that reproduce the population-wide covariability of large-scale neuronal recordings. We first confirmed that SNOPS accurately recovers simulated neural activity statistics. Then, we applied SNOPS to recordings in macaque visual and prefrontal cortices and discovered previously unknown limitations of spiking network models. Taken together, SNOPS can guide the development of network models, thereby enabling deeper insight into how networks of neurons give rise to brain function.