Abstract
The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode. This allows isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. However, correctly classifying spike waveforms into their underlying neuronal sources remains a challenge. This process, called spike sorting, typically consists of two steps: (1) extracting relevant waveform features (e.g., height, width), and (2) clustering them into non-overlapping groups believed to correspond to different neurons. In this study, we explored the performance of Gaussian mixture models (GMMs) in these two steps. We extracted relevant features using a combination of common techniques (e.g., principal components, wavelets) and GMM fitting parameters (e.g., Gaussian distances). Then, we developed an approach to perform unsupervised clustering using GMMs, estimating cluster properties in a data-driven way. We found the proposed GMM-based framework outperforms previously established methods in simulated and real extracellular recordings. We also discuss potentially better techniques for feature extraction than the widely used principal components. Finally, we provide a friendly graphical user interface to run our algorithm, which allows manual adjustments.
Highlights
The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode
Representing the data in terms of a principal component (PC) means that each waveform is redefined as a weighted sum of its values, a linear combination determined by the PC weights; this transforms the entire waveform into a single number
We found that PCA-based strategies had variable performance regardless of the information metric used, and presented a broad distribution of mean MInorm
Summary
The shape of extracellularly recorded action potentials is a product of several variables, such as the biophysical and anatomical properties of the neuron and the relative position of the electrode This allows isolating spikes of different neurons recorded in the same channel into clusters based on waveform features. It is necessary to reliably identify spikes of different neurons recorded from the same electrode In principle, this can be partially achieved because the extracellular waveform of action potentials varies depending on biophysical and morphological properties of the cells, as well as on the relative position of the electrode[1,2,3]. One can rescale the data in order to bias PCA to primarily extract multimodal components relevant for clustering, a technique called weighted-PCA10–12 (wPCA) In this framework, each time point (sample) of the waveform is divided by its variance (across waveforms) and multiplied by an estimate of its clustering separability. The challenge, is to find the optimal method to make such estimate in an unsupervised manner
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