Algorithms for Spike Sorting and Source Localization of Mice Neuron Signals

This study has developed a clustering algorithm for spike sorting. It assumes that
neuron signal waveform is different from each neuron when there is not overlapping
and bursting in neuron signal. Based on the previous hypothesis, tetrode, which is an
instrument detecting a neuron signal consisting of waveform resolution and spatial
resolution. Waveform that is detected from a tetrode is consisting of neuron signals and
noise. This study has pre-processed the linearity of tetrode waveforms, and filtered the
signal out high frequency component which usually is noise. In order to amplify the
difference between different neuron waveform, we further add the artifical detail
component into the waveform in time domain. This process will output waveform with
noise-reduced and detail-included. Finally, the principal component analysis (PCA) is
applied to reduce number of dimensionality. The feature which is extracted from these
previous methods is called LDPCA (low-pass difference PCA) feature. On the other
hand, the spatial resolution is defined as the decay which is a result of spatial distance
from neuron to tetrode based on signal transmitting model.
In clustering computing, an unsupervised clustering algorithm, affinity propagation
(AP), is employed, and the result of this algorithm can output an objective clustering
result. Even in low SNR environment (about 3 dB), the clustering accuracy is still
higher than 70%, and the accuracy is getting better when the SNR is getting higher.
Another clustering algorithm, k-means, can’t improve the accuracy even in higher SNR
environment (about 10 dB).
One of the advantages of the spike sorting is to express the model of neuron spikes
firing. An exponential probability distribution and the main parameter μ can be used to
describe the firing model. The false rate of μ is lower than 20% when the spike number
is more than 50.
Excepting the developed clustering algorithm, the method of signal source
localization based on planar tetrode signal has been developed. The co-planar tetrode is
virtually shifted from the 2-D tetrode to 3-D tetrode in this method. The error of
distance from localization can be considered as noise interference. In high SNR
environment, we can clearly observe the distribution of the localized points. The root
mean square error is less than 25 μm. The result shows that the method of localization
can help biological researchers to estimate the performance of spike sorting result.