Automatic spike sorting for neural decoding

While various automated spike sorting techniques have been developed, their impact on neural decoding has not been investigated. In this paper we extend previous Gaussian mixture models and expectation maximization (EM) techniques for automatic spike sorting. We suggest that good initialization of EM is critical and can be achieved via spectral clustering. To account for noise we extend the mixture model to include a uniform outlier process. Automatically determining the number of neurons recorded per electrode is a challenging problem which we solve using a greedy optimization algorithm that selects models with different numbers of neurons according to their decoding accuracy. We focus on data recorded from motor cortex and evaluate performance with respect to the decoding of hand kinematics from firing rates. We found that spike trains obtained by our automated technique result in more accurate neural decoding than those obtained by human experts.