Spike detection from noisy neural data in linear-probe recordings.
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.
Faculty of Informatics, Kogakuin University, 1-24-2 Nishi-Shinjuku, Shinjuku, Tokyo, 163-8677, Japan; Laboratory for Neural Circuit Theory, RIKEN Brain Science Institute, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan.
Takashi
T
Takekawa
Keisuke
K
Ota
Masanori
M
Murayama
Tomoki
T
Fukai
D000465:Algorithms; D000818:Animals; D001921:Brain / Q000502:physiology*; D004569:Electroencephalography / Q000379:methods*; D051379:Mice; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology*; D059629:Signal-To-Noise Ratio
diversified spike waveforms; error rate estimation; mouse; multi-electrode; peak distribution