Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Nat Comput Sci. 2024 Sep;4(9):690-705. doi: 10.1038/s43588-024-00688-3. Epub 2024 Sep 16.
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.
理解大脑功能可以通过构建能够准确再现大脑活动某些方面的计算模型来实现。尖峰神经元网络可以捕捉神经元回路的潜在生物物理学特性，但其活动对模型参数的依赖性却非常复杂。因此，人们已经使用启发式方法来配置尖峰网络模型，这可能导致无法发现足够复杂的活动状态来匹配大规模神经元记录。在这里，我们提出了一种自动程序，即使用群体统计数据的尖峰网络优化（SNOPS），用于定制能够再现大规模神经元记录的群体范围可变性的尖峰网络模型。我们首先确认 SNOPS 可以准确地恢复模拟神经活动的统计数据。然后，我们将 SNOPS 应用于猕猴视觉和前额叶皮层的记录，并发现了尖峰网络模型以前未知的局限性。总之，SNOPS 可以指导网络模型的开发，从而使我们能够更深入地了解神经元网络如何产生大脑功能。
