Predicting Spike Activity in Neuronal Cultures

be regarded as an abstraction of the underlying effective network connectivity, i.e. its functional connectivity. Although similar functional connectivity models have been described previously [2,3], we take a steepest descent approach to learn functional connectivity, which naturally allows for online learning and, hence, is able to capture network plasticity, i.e. changes in the structure. We use the log-likelihood of point processes as a criterion for optimization. This approach has previously been suggested to analyse neural receptive field plasticity [4]. Here we apply it to multi-channel recordings from neuronal cultures, and demonstrate its use for learning functional connectivity and predicting upcoming spike activity. A ROC curve analysis of our experiments shows that this online approach predicts upcoming spike activity very well. Acknowledgements This work was funded by the German Federal Ministry of Education and Research (BMBF grant 01GQ0420 to BCCN Freiburg). References

• Type: Conference
• Year: 2007
• Where: IJCNN
• Authors: Tayfun Gürel, Ulrich Egert, Steffen Kandler, Luc De Raedt, Stefan Rotter
• Keywords: Functional Connectivity, IJCNN 2007, Similar Functional Connectivity, Upcoming Spike Activity