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Sparse Coding and Compressive Sensing for Overlapping Neural Spike Sorting

Spike sorting is one of the key techniques to understand brain activity. With the development of modern electrophysiology technology, some recent multi-electrode technologies have been able to record the activity of thousands of neuronal spikes simultaneously. In this scenario, however, the recorded activity may be the overlap of multi-neuron spikes, which will degrade the sorting performance of existing cluster-based algorithms. In this paper, we introduce methods for overlapping spike sorting. The introduced methods start from a convolution model, where a sparse vector could be obtained via sparse coding or compressive sensing. Then, we use a maximum a posteriori estimate to optimize the sparse vector, which makes the overlapped spike sorting completed successfully. The advantage of the introduced method is that it performs better than traditional methods when the waveforms of the spikes are similar. In experiments, some synthetic and real spike data are used to testify the methods. The experiment results show that the introduced methods’ average sorting detection, defined as the ratio of successfully sorted spikes to the total spikes is nearly 4% higher than the traditional methods, under the condition of the experimental data with similar waveforms.