Inference of synaptic connectivity and external variability in neural microcircuits
Inference of synaptic connectivity and external variability in neural microcircuits
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DOI：
10.1007/s10827-020-00739-4
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发表时间：
2020-02-21
影响因子：
1.2
通讯作者：
Rosenbaum, Robert
中科院分区：
文献类型：
--
作者：
Baker, Cody;Froudarakis, Emmanouil;Rosenbaum, Robert
神经科学的一个主要目标是根据体内神经活动的大规模细胞外记录来估计神经连接。这在一定程度上具有挑战性，因为任何此类活动都是由网络的未测量的外部突触输入来调节的，这被称为常见输入问题。文献中提出了许多不同的功能连接测量方法，但它们与突触连接的直接关系经常被假设或忽略。对于体内数据，测量这种关系需要了解地面实况连接性，而这几乎总是不可用的。相反，许多研究使用计算机模拟作为调查基准，但此类方法必然依赖于有关模拟网络的各种简化假设，并且可能依赖于众多模拟参数。我们结合神经元网络模拟、数学分析和钙成像数据来解决何时以及如何理清功能连接、突触连接和潜在外部输入变异性的问题。我们通过数值和分析表明，尽管记录的尖峰活动的精度矩阵不能唯一地确定突触连接性，但实际上它通常与突触连接性密切相关。当共同考虑神经元变异的空间结构时，这种关系变得更加明显。
A major goal in neuroscience is to estimate neural connectivity from large scale extracellular recordings of neural activity in vivo. This is challenging in part because any such activity is modulated by the unmeasured external synaptic input to the network, known as the common input problem. Many different measures of functional connectivity have been proposed in the literature, but their direct relationship to synaptic connectivity is often assumed or ignored. For in vivo data, measurements of this relationship would require a knowledge of ground truth connectivity, which is nearly always unavailable. Instead, many studies use in silico simulations as benchmarks for investigation, but such approaches necessarily rely upon a variety of simplifying assumptions about the simulated network and can depend on numerous simulation parameters. We combine neuronal network simulations, mathematical analysis, and calcium imaging data to address the question of when and how functional connectivity, synaptic connectivity, and latent external input variability can be untangled. We show numerically and analytically that, even though the precision matrix of recorded spiking activity does not uniquely determine synaptic connectivity, it is in practice often closely related to synaptic connectivity. This relation becomes more pronounced when the spatial structure of neuronal variability is jointly considered.