Abstract
Prediction of animal’s behavior and detection of task relevant neural cliques using multi-spike trains are of great importance and challenges. We propose a robust and high accurate approach to classify multi-spike trains based on point process model and Bayesian rules. To detect task relevant neural cliques, a graph is constructed with its edge weights indicating the collaboration degree of neurons’ trail-to-trail response to tasks. Then minimum graph cut algorithm is introduced to detect neural cliques. Tested by data synchronously recorded in hippocampus during five sets of mouse U maze experiments (about 500 trails), the predicting accuracy is rather high and the statistical significance of the cliques is demonstrated.
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© 2011 Springer-Verlag Berlin Heidelberg
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Hu, F., Li, BM., Wei, H. (2011). Classification of Multi-spike Trains and Its Application in Detecting Task Relevant Neural Cliques. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_21
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DOI: https://doi.org/10.1007/978-3-642-24965-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24964-8
Online ISBN: 978-3-642-24965-5
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