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T-SNE visualization of large-scale neural recordings
  • George Dimitriadis,
  • Awaiting Activation,
  • Adam Kampff
George Dimitriadis
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Awaiting Activation
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Adam Kampff
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Abstract

Electrophysiology is entering the era of ‘Big Data’. Multiple probes, each with hundreds to thousands of individual electrodes, are now capable of simultaneously recording from many brain regions. The major challenge confronting these new technologies is transforming the raw data into physiologically meaningful signals, i.e. single unit spikes. Sorting the spike events of individual neurons from a spatiotemporally dense sampling of the extracellular electric field is a problem that has attracted much attention \cite{rey_past_2015,rossant_spike_2016}, but is still far from solved. Current methods still rely on human input and thus become unfeasible as the size of the data sets grow exponentially.
Here we introduce the t-student stochastic neighbor embedding (t-sne) dimensionality reduction method \cite{van_der_maaten_visualizing_2008} as a visualization tool in the spike sorting process. T-sne embeds the n-dimensional extracellular spikes (n = number of features by which each spike is decomposed) into a low (usually two) dimensional space. We show that such embeddings, even starting from different feature spaces, form obvious clusters of spikes that can be easily visualized and manually delineated with a high degree of precision. We propose that these clusters represent single units and test this assertion by applying our algorithm on labeled data sets both from hybrid \cite{rossant_spike_2016} and paired juxtacellular/extracellular recordings \cite{neto_validating_2016}. We have released a graphical user interface (gui) written in python as a tool for the manual clustering of the t-sne embedded spikes and as a tool for an informed overview and fast manual curation of results from other clustering algorithms. Furthermore, the generated visualizations offer evidence in favor of the use of probes with higher density and smaller electrodes. They also graphically demonstrate the diverse nature of the sorting problem when spikes are recorded with different methods and arise from regions with different background spiking statistics.