Spike-Triggered Regression for Synaptic Connectivity Reconstruction in Neuronal Networks.
Spike-Triggered Regression for Synaptic Connectivity Reconstruction in Neuronal Networks.
复制标题
神经网络中突触连接重建的尖峰触发回归
DOI：
10.3389/fncom.2017.00101
复制
发表时间：
2017
影响因子：
3.2
通讯作者：
Cai D
中科院分区：
文献类型：
--
作者：
Zhang Y;Xiao Y;Zhou D;Cai D
神经元如何在大脑中连接起来进行计算是神经科学的一个关键问题。近年来，钙成像和多电极阵列技术的发展极大地提高了我们在单细胞水平上测量神经元群放电活动的能力。同时，细胞内记录技术能够测量神经元的阈下电压动态。我们的工作解决了如何结合这些测量来揭示潜在网络结构的问题。我们提出了尖峰触发回归（STR）方法，该方法利用神经元群的电压跟踪和放电活动来重建潜在的突触连通性。我们对基于电导的集火神经网络的数值研究表明，只需要20 ~ 100秒的短数据就可以准确地恢复网络拓扑和相应的耦合强度。我们的方法即使在密集连接和几乎同步动态的情况下也能准确地重建大型神经网络，这是许多其他网络重建方法无法成功处理的。此外，我们指出，对于稀疏网络，STR方法可以在缺乏所有其他神经元的全局信息的情况下，高精度地推断出每对神经元之间的耦合强度。
How neurons are connected in the brain to perform computation is a key issue in neuroscience. Recently, the development of calcium imaging and multi-electrode array techniques have greatly enhanced our ability to measure the firing activities of neuronal populations at single cell level. Meanwhile, the intracellular recording technique is able to measure subthreshold voltage dynamics of a neuron. Our work addresses the issue of how to combine these measurements to reveal the underlying network structure. We propose the spike-triggered regression (STR) method, which employs both the voltage trace and firing activity of the neuronal population to reconstruct the underlying synaptic connectivity. Our numerical study of the conductance-based integrate-and-fire neuronal network shows that only short data of 20 ~ 100 s is required for an accurate recovery of network topology as well as the corresponding coupling strength. Our method can yield an accurate reconstruction of a large neuronal network even in the case of dense connectivity and nearly synchronous dynamics, which many other network reconstruction methods cannot successfully handle. In addition, we point out that, for sparse networks, the STR method can infer coupling strength between each pair of neurons with high accuracy in the absence of the global information of all other neurons.
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影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1093/cercor/bhu252
发表时间：
2015-10
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Shimono M;Beggs JM
通讯作者：
Beggs JM
影响因子：
5.7
作者：
Callaway EM
通讯作者：
Callaway EM
DOI：
10.1007/978-1-61737-992-5_6
发表时间：
2011-01-01
期刊：
MAGNETIC RESONANCE NEUROIMAGING: METHODS AND PROTOCOLS
影响因子：
--
作者：
Jones, Derek K.;Leemans, Alexander
通讯作者：
Leemans, Alexander
影响因子：
1.2
作者：
Rangan, Aaditya V.;Cai, David
通讯作者：
Cai, David