Robust spike sorting of retinal ganglion cells tuned to spot stimuli.

We propose an automatic spike sorting approach for the data recorded from a microelectrode array during visual stimulation of wild type retinas with tiled spot stimuli. The approach first detects individual spikes per electrode by their signature local minima. With the mixture probability distribution of the local minima estimated afterwards, it applies a minimum-squared-error clustering algorithm to sort the spikes into different clusters. A template waveform for each cluster per electrode is defined, and a number of reliability tests are performed on it and its corresponding spikes. Finally, a divisive hierarchical clustering algorithm is used to deal with the correlated templates per cluster type across all the electrodes. According to the measures of performance of the spike sorting approach, it is robust even in the cases of recordings with low signal-to-noise ratio.