Departament d'Estructura i Consituents de la Matèria, Universitat de Barcelona, Barcelona, Spain.
Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany; Georg-August-Universität, Physics Department, Göttingen, Germany; Bernstein Center for Computational Neuroscience, Göttingen, Germany.
PLoS One. 2014 Jun 6;9(6):e98842. doi: 10.1371/journal.pone.0098842. eCollection 2014.
Neuronal dynamics are fundamentally constrained by the underlying structural network architecture, yet much of the details of this synaptic connectivity are still unknown even in neuronal cultures in vitro. Here we extend a previous approach based on information theory, the Generalized Transfer Entropy, to the reconstruction of connectivity of simulated neuronal networks of both excitatory and inhibitory neurons. We show that, due to the model-free nature of the developed measure, both kinds of connections can be reliably inferred if the average firing rate between synchronous burst events exceeds a small minimum frequency. Furthermore, we suggest, based on systematic simulations, that even lower spontaneous inter-burst rates could be raised to meet the requirements of our reconstruction algorithm by applying a weak spatially homogeneous stimulation to the entire network. By combining multiple recordings of the same in silico network before and after pharmacologically blocking inhibitory synaptic transmission, we show then how it becomes possible to infer with high confidence the excitatory or inhibitory nature of each individual neuron.
神经元的动力学从根本上受到基础结构网络架构的限制，但即使在体外培养的神经元中，突触连接的许多细节仍然未知。在这里，我们扩展了先前基于信息理论的广义传递熵的方法，将其应用于兴奋性和抑制性神经元模拟神经网络的连接重建。我们表明，由于所开发的度量方法是无模型的，如果同步爆发事件之间的平均发放率超过小的最小频率，则这两种连接都可以可靠地推断出来。此外，我们基于系统模拟建议，即使在爆发之间的自发速率较低的情况下，通过对整个网络施加弱的空间均匀刺激，也可以提高满足我们的重建算法的要求。通过在药理学上阻断抑制性突触传递前后对相同的计算机网络进行多次记录，我们展示了如何以高置信度推断每个神经元的兴奋性或抑制性本质。
