Spike detection methods for polytrodes and high density microelectrode arrays.

This paper compares the ability of different methods to detect and resolve spikes recorded extracellularly with polytrode and high-density microelectrode arrays (MEAs). Detecting spikes on such arrays is more complex than with single electrodes or tetrodes since a single spike from a neuron may cause threshold crossings on several adjacent channels, giving rise to multiple events. These initial events have to be recognized as belonging to a single spike. Combining them is, in essence, a clustering problem. A conflicting need is to be able to resolve spike waveforms that occur close together in space and time. We first evaluated three different detection methods, using simulated data in which spike shape waveforms obtained from real recordings were added to noise with an amplitude and temporal structure similar to that found in real recordings. Performance was assessed by calculating the percentage of correctly identified spikes vs. the false positive rate. Using the best of these detection methods, two different methods for avoiding multiple detections per spike were tested: one based on windowing and the other based on clustering. Using parameters that avoided spatial and temporal duplication, the spatiotemporal resolution of the two methods was next evaluated. The method based on clustering gave slightly better results. Both methods could resolve spikes occurring 1 ms or more apart, regardless of their spatial separation. There was no restriction on the temporal resolution of spike pairs for units more than 200 μm apart.