A firing rate independent threshold estimation for neuronal spike detection methods

Setting the threshold for a spike detection method is always challenging. The amplitude of the spikes that can be recognized really depends on the noise present in the recording and the threshold must set a good tradeoff between the false detection of noise fluctuations and the smaller spikes. In this work we demonstrate how common approaches to automatic threshold estimation are highly affected by changes in the firing rate of neurons, causing the threshold to become an unreliable yardstick of evaluation between different channels and even within the same recording. For this reason, here we propose a noise estimation method relying on the base 2 logarithm, suitable for the automatic setting of a firing-rate independent threshold for spike detection methods. We compare its performances against other statistical variability indices as the widely used RMS, the mean, and the median of the absolute value of the signal both qualitatively and quantitatively, while varying the firing rate exhibited by the testbench dataset. Our results indicate that while the RMS and the mean are heavily affected by the firing rate, our proposed method performs almost equally to the median-based estimation, but the complexity of the latter poses it as an optimal solution for applications requiring a real-time definition of the threshold.