Abstract
This study introduces a new spike sorting method that classifies spike waveforms from multiunit recordings into spike trains of individual neurons. In particular, we develop a method to sort a spike mixture generated by a heterogeneous neural population. Such a spike sorting has a significant practical value, but was previously difficult. The method combines a feature extraction method, which we may term “multimodality-weighted principal component analysis” (mPCA), and a clustering method by variational Bayes for Student's t mixture model (SVB). The performance of the proposed method was compared with that of other conventional methods for simulated and experimental data sets. We found that the mPCA efficiently extracts highly informative features as clusters clearly separable in a relatively low-dimensional feature space. The SVB was implemented explicitly without relying on Maximum-A-Posterior (MAP) inference for the “degree of freedom” parameters. The explicit SVB is faster than the conventional SVB derived with MAP inference and works more reliably over various data sets that include spiking patterns difficult to sort. For instance, spikes of a single bursting neuron may be separated incorrectly into multiple clusters, whereas those of a sparsely firing neuron tend to be merged into clusters for other neurons. Our method showed significantly improved performance in spike sorting of these “difficult” neurons. A parallelized implementation of the proposed algorithm (EToS version 3) is available as open-source code at http://etos.sourceforge.net/.
Highlights
Since a vast number of neurons are simultaneously active in the brain, the analyses of action potentials of multiple neurons are crucial for uncovering the principle of brain computation
MATERIALS AND METHODS Figure 1 summarizes the major steps of the algorithms tested in this study: (1) detecting and clipping out spike candidates via amplitude thresholding of a high-pass filtered signal and a window function; (2) applying wavelet transform (WT) to the spike waveforms; (3) extracting the features of the spike waveforms in the feature space spanned by the wavelet coefficients; (4) classifying the extracted features to identify spikes belonging to single neurons
In order to improve the accuracy and speed of spike sorting, we have proposed a new algorithm based on multimodality-weighted principal component analysis” (mPCA) and explicit SVB and compared the performance with several other spikesorting methods on artificial and experimental spike data
Summary
Since a vast number of neurons are simultaneously active in the brain, the analyses of action potentials (spikes) of multiple neurons are crucial for uncovering the principle of brain computation. The extracellularly recorded data contains spikes of many neurons surrounding the tip of electrodes, and all spike-like signals belonging to a single neuron have to be correctly labeled as activity of the same neuron. This process, known as spike sorting (Lewicki, 1998; Brown et al, 2004; Buzsáki, 2004), consists of three major steps: the first step to detect spike candidates, the second step to extract the features of spikes, and the third step to classify the extracted features (Abeles, 1982; Csicsvari et al, 1998; Wood et al, 2004). There is no guarantee that the data is classified into well-separated clusters in the directions of large variances
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