Wu Shenghao, Huang Chengcheng, Snyder Adam, Smith Matthew, Doiron Brent, Yu Byron
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
bioRxiv. 2023 Sep 22:2023.09.21.558920. doi: 10.1101/2023.09.21.558920.
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 the dependence of their activity 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 and thereby enable deeper insight into how networks of neurons give rise to brain function.
通过构建能够准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。脉冲神经元网络捕捉了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极为复杂。因此，启发式方法被用于配置脉冲网络模型，这可能导致无法发现足够复杂的活动模式以匹配大规模神经元记录。在此，我们提出一种自动程序——利用群体统计的脉冲网络优化（SNOPS），用于定制能够再现大规模神经元记录的全群体协变性的脉冲网络模型。我们首先证实SNOPS能够准确恢复模拟神经活动统计数据。然后，我们将SNOPS应用于猕猴视觉和前额叶皮层的记录，并发现了脉冲网络模型先前未知的局限性。综上所述，SNOPS可以指导网络模型的开发，从而更深入地了解神经元网络如何产生大脑功能。
