Decoding Neuronal Networks: A Reservoir Computing Approach for Predicting Connectivity and Functionality
arXiv (Cornell University)(2023)
摘要
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.
更多查看译文
关键词
forecasting neuronal
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要