Prediction on Spike Data Using Kernel Algorithms

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Jan Eichhorn, Andreas Tolias, Alexander Zien, Malte Kuss, Jason Weston, Nikos Logothetis, Bernhard Schölkopf, Carl Rasmussen


We report and compare the performance of different learning algorithms based on data from cortical recordings. The task is to predict the orienta- tion of visual stimuli from the activity of a population of simultaneously recorded neurons. We compare several ways of improving the coding of the input (i.e., the spike data) as well as of the output (i.e., the orienta- tion), and report the results obtained using different kernel algorithms.