On the use of dynamic Bayesian networks in reconstructing functional neuronal networks from spike train ensembles.
On the use of dynamic Bayesian networks in reconstructing functional neuronal networks from spike train ensembles.
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DOI：
10.1162/neco.2009.11-08-900
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发表时间：
2010-01
影响因子：
2.9
通讯作者：
Oweiss KG
中科院分区：
文献类型：
--
作者：
Eldawlatly S;Zhou Y;Jin R;Oweiss KG
皮层神经元之间的协调被认为是调节许多高级皮层过程的关键因素，如感知、注意、学习和记忆的形成。推断这种协调下的神经回路的拓扑结构对于描述复杂刺激下皮质神经元之间高度非线性的、时变的相互作用是重要的。在这项工作中，我们研究了动态贝叶斯网络(DBN)在从观察到的棘波序列推断皮层神经元之间的有效连通性方面的适用性。我们证明，DBNS可以推断这些神经元之间潜在的非线性和时变的因果相互作用，并可以在某些约束下区分它们之间的单突触和多突触联系，这些联系可能是连接的。我们分析了条件泊松棘波训练数据，模拟了小尺寸和中大型皮层网络的棘波活动。在网络结构的系统变化下，该方法的性能被评估并与其他方法进行比较，以模拟在大脑皮层中通常观察到的广泛的反应。结果表明，DBN在推断大脑皮层网络中的有效连通性方面是有效的。
Coordination among cortical neurons is believed to be key element in mediating many high level cortical processes such as perception, attention, learning and memory formation. Inferring the topology of the neural circuitry underlying this coordination is important to characterize the highly non-linear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of Dynamic Bayesian Networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying non-linear and time-varying causal interactions between these neurons and can discriminate between mono and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally-Poisson spike train data mimicking spiking activity of cortical networks of small and moderately-large sizes. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks.
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影响因子：
3
作者：
Dahlhaus, R;Eichler, M;Sandkuhler, J
通讯作者：
Sandkuhler, J
影响因子：
2.3
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通讯作者：
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作者：
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