Spike-feature based estimation of electrode position in extracellular neural recordings

Abstract

Detecting and sorting spikes in extracellular neural recordings are common procedures in assessing the activity of individual neurons. In chronic recordings, passive electrode movements introduce changes in the shape of detected spike waveforms, and may thus lead to problems with identification and tracking of spikes recorded at separate instances in time, which is an important step in long-term monitoring of individual neurons. Information about electrode movements after implantation is crucial to the evaluation of mechanical stability of different electrode designs. In this paper, we present a preliminary study of the relationship between electrode movements and the resulting movements of spike-features in feature space. We show that there is a characteristic relationship between the two movements and that this relationship can be modeled as a linear transformation between two coordinate systems. Finally, we show how the relationship can be used for estimating electrode positions based on measured spike waveforms without any prior knowledge about the type of neuron by introducing a learning procedure during electrode insertion.