In this study, we address the challenge of analyzing electrophysiological measurements in neuronal networks. Our computational model, based on the Reservoir Computing Network (RCN) architecture, deciphers spatio-temporal data obtained from electrophysiological measurements of neuronal cultures. By reconstructing the network structure on a macroscopic scale, we reveal the connectivity between neuronal units. Notably, our model outperforms common methods like Cross-Correlation and Transfer-Entropy in predicting the network's connectivity map. Furthermore, we experimentally validate its ability to forecast network responses to specific inputs, including localized optogenetic stimuli.

翻译：在本研究中，我们致力于解决神经元网络电生理测量分析所面临的挑战。基于储层计算网络架构的计算模型，能够破解从神经元培养物电生理测量中获得的时空数据。通过在宏观尺度上重建网络结构，我们揭示了神经元单元之间的连接性。值得注意的是，该模型在预测网络连接图谱方面优于互相关和传递熵等传统方法。此外，我们通过实验验证了该模型预测网络对特定输入（包括局部光遗传刺激）响应的能力。