Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. While previous methods often treat the detection of each putative connection as a separate hypothesis test, here we develop a modeling approach that infers synaptic connections while incorporating circuit properties learned from the whole network. We use an extension of the Generalized Linear Model framework to describe the cross-correlograms between pairs of neurons and separate correlograms into two parts: a slowly varying effect due to background fluctuations and a fast, transient effect due to the synapse. We then use the observations from all putative connections in the recording to estimate two network properties: the presynaptic neuron type (excitatory or inhibitory) and the relationship between synaptic latency and distance between neurons. Constraining the presynaptic neuron's type, synaptic latencies, and time constants improves synapse detection. In data from simulated networks, this model outperforms two previously developed synapse detection methods, especially on the weak connections. We also apply our model to in vitro multielectrode array recordings from mouse somatosensory cortex. Here our model automatically recovers plausible connections from hundreds of neurons, and the properties of the putative connections are largely consistent with previous research.

中文翻译：

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从带有延迟和类型约束的峰值记录中基于模型的推定突触连接检测

使用大规模细胞外尖峰记录检测突触连接提出了统计学上的挑战。尽管以前的方法通常将每个推定连接的检测视为一个单独的假设检验，但在这里我们开发了一种建模方法，该方法可以推断突触连接，同时结合从整个网络中学到的电路特性。我们使用广义线性模型框架的扩展来描述成对的神经元之间的互相关图和独立的相关图分为两部分：由于背景波动而产生的缓慢变化的影响和由于突触引起的快速而短暂的影响。然后，我们使用来自记录中所有推定连接的观察值来估计两个网络属性：突触前神经元类型（兴奋性或抑制性）以及突触潜伏期与神经元之间距离之间的关系。限制突触前神经元的类型，突触潜伏期和时间常数可改善突触检测。在来自仿真网络的数据中，该模型优于两种以前开发的突触检测方法，尤其是在弱连接上。我们还将我们的模型应用于小鼠体感皮层的体外多电极阵列记录。在这里，我们的模型会自动从数百个神经元中恢复合理的连接，并且推定连接的属性在很大程度上与先前的研究一致。该模型优于以前开发的两种突触检测方法，尤其是在弱连接上。我们还将我们的模型应用于小鼠体感皮层的体外多电极阵列记录。在这里，我们的模型会自动从数百个神经元中恢复合理的连接，并且推定连接的属性在很大程度上与先前的研究一致。该模型优于以前开发的两种突触检测方法，尤其是在弱连接上。我们还将我们的模型应用于小鼠体感皮层的体外多电极阵列记录。在这里，我们的模型会自动从数百个神经元中恢复合理的连接，并且推定连接的属性在很大程度上与先前的研究一致。