  Spikes can be easily detected inmostintracellular recordings as sharp peaks.
However, insome experimental preparations,because of unipolar morphology or
other characteristicsof the recorded neurons, the sizes of the spikes recorded
from the soma can be much smaller. The experimental settings and the quality of
the recording can also affect the observed amplitudes of the spikes. Whole-cell
patch-clamp recordings from the somata of projection neurons of the antennal
lobe in Drosophila or mosquitoes can show spikes with amplitudes as small as 2
mV. Moreover, the observed spikes often ride on relatively large
depolarizations, which makes it difficult for the standard thresholding-based
approaches to distinguish them from noise or sharp EPSPs present in the signal.
For spike detection in such neuronal recordings, we propose a clustering-based
algorithm that separates peaks corresponding to action potentials from those
corresponding to noise. Candidate peaks, including many noise peaks, are first
selected according to their sharpness, and then a feature vector is extracted
for each peak. The 3-dimensional feature vector contains the absolute value of
the peak voltage, height of the spike, and the magnitude of the second
derivative minima attained during the spike. In most recordings, this 3D space
reveals two natural clusters, separating the noise peaks from the true action
potentials. Some parameters of the algorithm can be optionally altered by the
user to improve detection, which comes handy in the few recordings where the
default parameters do not work well. In summary, the algorithm facilitates
accurate spike detection to enable the interpretation and analysis of
patch-clamp data from neuronal recordings in invertebrates. The algorithm is
implemented as an freely available open-source tool.
