Abstract
Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike.
Highlights
Extracellular (EC) recordings of action potentials yield ‘‘spikes’’ from a population of neurons in the vicinity of the recording electrode
In this paper we show that recordings from a laminar electrode array can be used to recover the waveform of EC action potentials of hippocampal pyramidal cells from spike recordings embedded in LFP, without using frequency selective filters
In our recordings, the positive bump following the spike disappeared when we used our multiple regression method instead of filtering, indicating that the positive bump seen in the filtered traces was merely an artifact generated intrinsically in the filter, at least in these cases. This result does not prove that the late part of any apparently biphasic HP-filtered EC spike waveform is always purely a filter artifact, but our analysis indicates that such filtering artifacts occur and can be quite prominent, and that they can be eliminated by the multiple regression method
Summary
Extracellular (EC) recordings of action potentials yield ‘‘spikes’’ from a population of neurons in the vicinity of the recording electrode. These spikes are embedded in background voltage fluctuations known as the ‘‘local field potential’’ (LFP). In order to study the function of individual neurons with this method, the spikes must be extracted from the LFP and separated into groups of spikes originating from the same neuron. This separtation requires unsupervised pattern classification (data clustering), which in this context is often called ‘‘spike sorting’’ [1,2]. In this paper we will show that the fundamental assumption that ‘‘spikes’’ and LFP are spectrally distinct cannot generally be made
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