Graduate School of Informatics, Kyoto University, Kyoto, 606-8501, Japan.
Mathematics and Informatics Center, The University of Tokyo, Tokyo, 113-8656, Japan.
Sci Rep. 2021 Jun 8;11(1):12087. doi: 10.1038/s41598-021-91244-w.
The recent increase in reliable, simultaneous high channel count extracellular recordings is exciting for physiologists and theoreticians because it offers the possibility of reconstructing the underlying neuronal circuits. We recently presented a method of inferring this circuit connectivity from neuronal spike trains by applying the generalized linear model to cross-correlograms. Although the algorithm can do a good job of circuit reconstruction, the parameters need to be carefully tuned for each individual dataset. Here we present another method using a Convolutional Neural Network for Estimating synaptic Connectivity from spike trains. After adaptation to huge amounts of simulated data, this method robustly captures the specific feature of monosynaptic impact in a noisy cross-correlogram. There are no user-adjustable parameters. With this new method, we have constructed diagrams of neuronal circuits recorded in several cortical areas of monkeys.
最近可靠的、同时具有高通道计数的细胞外记录的增加，让生理学家和理论家感到兴奋，因为它提供了重建潜在神经元回路的可能性。我们最近提出了一种通过将广义线性模型应用于互相关图来从神经元尖峰序列推断这种回路连接的方法。尽管该算法可以很好地进行电路重建，但对于每个数据集，参数都需要仔细调整。在这里，我们提出了另一种使用卷积神经网络从尖峰序列估计突触连接的方法。在适应大量模拟数据后，该方法在嘈杂的互相关图中稳健地捕获了单突触影响的特定特征。没有用户可调整的参数。使用这种新方法，我们构建了在猴子的几个皮质区域记录的神经元回路的图谱。
