Multi-Electrode Spike Sorting by Clustering Transfer Functions

Part of Advances in Neural Information Processing Systems 11 (NIPS 1998)

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Dmitry Rinberg, Hanan Davidowitz, Naftali Tishby


A new paradigm is proposed for sorting spikes in multi-electrode data using ratios of transfer functions between cells and electrodes. It is assumed that for every cell and electrode there is a stable linear relation. These are dictated by the properties of the tissue, the electrodes and their relative geometries. The main advantage of the method is that it is insensitive to variations in the shape and amplitude of a spike. Spike sorting is carried out in two separate steps. First, templates describing the statistics of each spike type are generated by clustering transfer function ratios then spikes are detected in the data using the spike statistics. These techniques were applied to data generated in the escape response system of the cockroach.