eprintid: 10052284
rev_number: 19
eprint_status: archive
userid: 608
dir: disk0/10/05/22/84
datestamp: 2018-07-16 09:39:28
lastmod: 2021-09-26 22:59:06
status_changed: 2018-07-16 09:39:28
type: article
metadata_visibility: show
creators_name: Dimitriadis, G
creators_name: Neto, JP
creators_name: Kampff, AR
title: t-SNE Visualization of Large-Scale Neural Recordings
ispublished: pub
divisions: UCL
divisions: B02
divisions: C08
divisions: D75
keywords: Science & Technology, Technology, Life Sciences & Biomedicine, Computer Science, Artificial Intelligence, Neurosciences, Computer Science, Neurosciences & Neurology, NONLINEAR DIMENSIONALITY REDUCTION, SPIKE SORTING ALGORITHMS, MULTIUNIT RECORDINGS, VARIABILITY, NEURONS
note: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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, that is, 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 (Rey, Pedreira, & Quian Quiroga, 2015; Rossant et al., 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 grows exponentially. Here we introduce the -student stochastic neighbor embedding (t-SNE) dimensionality reduction method (Van der Maaten & Hinton, 2008) as a visualization tool in the spike sorting process. t-SNE embeds the -dimensional extracellular spikes ( = 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 from both hybrid (Rossant et al., 2016) and paired juxtacellular/extracellular recordings (Neto et al., 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 different 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.
date: 2018-07-01
date_type: published
publisher: MIT PRESS
official_url: http://dx.doi.org/10.1162/neco_a_01097
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1563522
doi: 10.1162/neco_a_01097
lyricists_name: Dimitriadis, George
lyricists_name: Kampff, Adam
lyricists_id: GDIMI68
lyricists_id: RKAMP49
actors_name: Dimitriadis, George
actors_id: GDIMI68
actors_role: owner
full_text_status: public
publication: Neural Computation
volume: 30
number: 7
pagerange: 1750-1774
pages: 25
issn: 1530-888X
citation:        Dimitriadis, G;    Neto, JP;    Kampff, AR;      (2018)    t-SNE Visualization of Large-Scale Neural Recordings.                   Neural Computation , 30  (7)   pp. 1750-1774.    10.1162/neco_a_01097 <https://doi.org/10.1162/neco_a_01097>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10052284/1/George%20Dimitriadis.pdf