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Optimal sorting of neural spikes with wavelet and filtering techniques

We show that robustness of sorting of neural spikes using the wavelet transform depends strongly on the statistics of experimental noise and the characteristic time scales of spike waveforms. Incorporating adaptive filtering of the extracellular potential into the wavelet sorting algorithm we propose a novel method, the Parametric Wavelet sorting with Advanced Filtering (PWAF), whose classification error approaches the theoretical minimum. Efficiency of the proposed technique is proved with both simulated and real electrophysiological recordings.