Spike sorting with Gaussian mixture models

Souza, Bryan C.; Lopes-dos-Santos, Vítor; Bacelo, João; Tort, Adriano Bretanha Lopes

Spike sorting with Gaussian mixture models

Página do item simplificado Estatísticas

dc.contributor.author	Souza, Bryan C.
dc.contributor.author	Lopes-dos-Santos, Vítor
dc.contributor.author	Bacelo, João
dc.contributor.author	Tort, Adriano Bretanha Lopes
dc.date.accessioned	2019-03-13T16:53:34Z
dc.date.available	2019-03-13T16:53:34Z
dc.date.issued	2019-03-06
dc.description.resumo	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.	pt_BR
dc.identifier.citation	SOUZA, B. C. et al. Spike sorting with Gaussian mixture models. Sci Rep. v. 9, p. 3627, mar. 2019. Doi: 10.1038/s41598-019-39986-6	pt_BR
dc.identifier.doi	10.1038/s41598-019-39986-6
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/26757
dc.language	en	pt_BR
dc.subject	spike sorting	pt_BR
dc.subject	Gaussian mixture models	pt_BR
dc.subject	computational neuroscience	pt_BR
dc.title	Spike sorting with Gaussian mixture models	pt_BR
dc.type	article	pt_BR