Electrophysiological nature of neuronal networks allows to reveal various interactions between different cell units at a very short time-scales. One of the many challenges in analyzing these signals is to retrieve the morphology and functionality of a given network. In this work we developed a computational model, based on Reservoir Computing Network (RCN) architecture, which decodes the spatio-temporal data from electro-physiological measurements of neuronal cultures and reconstructs the network structure on a macroscopic domain, representing the connectivity between neuronal units. We demonstrate that the model can predict the connectivity map of the network with higher accuracy than the common methods such as Cross-Correlation and Transfer-Entropy. In addition, we experimentally demonstrate the ability of the model to predict a network response to a specific input, such as localized stimulus.

翻译：神经元网络的电生理特性使我们能够在极短时间尺度上揭示不同细胞单元之间的多种相互作用。分析这些信号面临的众多挑战之一，是恢复给定网络的形态结构与功能特性。本研究开发了一种基于储备池计算网络（RCN）架构的计算模型，该模型能够解码神经元培养物电生理测量的时空数据，并在宏观层面上重建代表神经元单元之间连接性的网络结构。我们证明，与互相关和传递熵等常用方法相比，该模型能以更高精度预测网络的连接图谱。此外，我们还通过实验验证了该模型预测网络对特定输入（如局部刺激）产生响应的能力。