Department of Psychological Sciences, University of Connecticut, Storrs, Connecticut.
Santa Cruz Institute for Particle Physics, University of California, Santa Cruz, California.
J Neurophysiol. 2020 Dec 1;124(6):1588-1604. doi: 10.1152/jn.00066.2020. Epub 2020 Sep 16.
Detecting synaptic connections using large-scale extracellular spike recordings presents a statistical challenge. Although 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 the 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. Detecting synaptic connections using large-scale extracellular spike recordings is a difficult statistical problem. Here, we develop an extension of a generalized linear model that explicitly separates fast synaptic effects and slow background fluctuations in cross-correlograms between pairs of neurons while incorporating circuit properties learned from the whole network. This model outperforms two previously developed synapse detection methods in the simulated networks and recovers plausible connections from hundreds of neurons in in vitro multielectrode array data.
使用大规模细胞外尖峰记录来检测突触连接是一个统计学上的挑战。虽然以前的方法通常将每个假定的连接的检测视为一个独立的假设检验，但我们在这里开发了一种建模方法，该方法在整合从整个网络学习到的电路特性的同时推断突触连接。我们使用广义线性模型框架的扩展来描述成对神经元之间的互相关图，并将相关图分为两部分：由于背景波动而产生的缓慢变化的效应和由于突触而产生的快速瞬态效应。然后，我们使用记录中所有假定连接的观测值来估计两个网络特性：前神经元类型（兴奋性或抑制性）和突触潜伏期与神经元之间距离之间的关系。约束前神经元的类型、突触潜伏期和时间常数可以提高突触检测的性能。在模拟网络的数据中，该模型优于两种以前开发的突触检测方法，尤其是在弱连接的情况下。我们还将我们的模型应用于体外多电极阵列记录的小鼠体感皮层。在这里，我们的模型可以自动从数百个神经元中恢复合理的连接，并且假定连接的特性与以前的研究基本一致。使用大规模细胞外尖峰记录来检测突触连接是一个具有挑战性的统计问题。在这里，我们开发了广义线性模型的扩展，该扩展明确分离了成对神经元之间互相关图中的快速突触效应和缓慢背景波动，同时整合了从整个网络学习到的电路特性。该模型在模拟网络中优于两种以前开发的突触检测方法，并从体外多电极阵列数据中的数百个神经元中恢复合理的连接。
