Spike detection from noisy neural data in linear-probe recordings

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Abstract

Simultaneous recordings of multiple neuron activities with multi-channel extracellular electrodes are widely used for studying information processing by the brain's neural circuits. In this method, the recorded signals containing the spike events of a number of adjacent or distant neurons must be correctly sorted into spike trains of individual neurons, and a variety of methods have been proposed for this spike sorting. However, spike sorting is computationally difficult because the recorded signals are often contaminated by biological noise. Here, we propose a novel method for spike detection, which is the first stage of spike sorting and hence crucially determines overall sorting performance. Our method utilizes a model of extracellular recording data that takes into account variations in spike waveforms, such as the widths and amplitudes of spikes, by detecting the peaks of band-pass-filtered data. We show that the new method significantly improves the cost–performance of multi-channel electrode recordings by increasing the number of cleanly sorted neurons.

We introduce a spike detection method based on a model of extracellular recording data that takes into account variations in spike waveforms, such as the widths and amplitudes of spikes. The proposed method can evaluate appropriate thresholds using the distributions of the peaks of band-pass-filtered data.

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