Model-based detection of putative synaptic connections from spike recordings with latency and type constraints
Model-based detection of putative synaptic connections from spike recordings with latency and type constraints
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基于模型检测来自具有延迟和类型限制的尖峰记录的假定突触连接
DOI：
10.1152/jn.00066.2020
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发表时间：
2020
影响因子：
2.5
通讯作者：
Stevenson, Ian H.
中科院分区：
文献类型：
--
作者：
Ren, Naixin;Ito, Shinya;Hafizi, Hadi;Beggs, John M.;Stevenson, Ian H.
使用大规模细胞外尖峰记录检测突触连接提出了统计挑战。尽管以前的方法通常将每个假定连接的检测视为单独的假设检验，但在这里我们开发了一种建模方法，可以推断突触连接，同时结合从整个网络学到的电路属性。我们使用广义线性模型框架的扩展来描述神经元对之间的互相关图，并将相关图分成两部分：由于背景波动引起的缓慢变化的效应和由于突触引起的快速瞬态效应。然后，我们使用记录中所有假定连接的观察结果来估计两个网络属性：突触前神经元类型（兴奋性或抑制性）以及突触潜伏期和神经元之间距离之间的关系。限制突触前神经元的类型、突触延迟和时间常数可以改善突触检测。在模拟网络的数据中，该模型优于之前开发的两种突触检测方法，尤其是在弱连接方面。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，我们的模型自动恢复来自数百个神经元的合理连接，并且假定连接的属性与之前的研究基本一致。新的和值得注意的使用大规模细胞外尖峰记录检测突触连接是一个困难的统计问题。在这里，我们开发了广义线性模型的扩展，该模型明确地将神经元对之间的交叉相关图中的快速突触效应和缓慢的背景波动分开，同时结合从整个网络学到的电路特性。该模型在模拟网络中优于两种先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恢复了合理的连接。
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.NEW & NOTEWORTHYDetecting 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.
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影响因子：
16.6
作者：
Levenstein, Daniel;Buzsaki, Gyorgy;Rinzel, John
通讯作者：
Rinzel, John
影响因子：
5.3
作者：
Ghanbari, Abed;Ren, Naixin;Stevenson, Ian H.
通讯作者：
Stevenson, Ian H.
影响因子：
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作者：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
Zador AM
影响因子：
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作者：
Ito S;Yeh FC;Hiolski E;Rydygier P;Gunning DE;Hottowy P;Timme N;Litke AM;Beggs JM
通讯作者：
Beggs JM