Performance evaluation of PCA-based spike sorting algorithms

2008 ◽  
Vol 91 (3) ◽  
pp. 232-244 ◽  
Author(s):  
Dimitrios A. Adamos ◽  
Efstratios K. Kosmidis ◽  
George Theophilidis
Keyword(s):  
Performance Evaluation ◽  
Spike Sorting ◽  
Sorting Algorithms

Performance evaluation of LAN sorting algorithms

1987 ◽  
Vol 15 (1) ◽  
pp. 226-233
Author(s):  
Mohamed Salehmohamed ◽  
W. S. Luk ◽  
Joseph G. Peters
Keyword(s):  
Performance Evaluation ◽  
Sorting Algorithms

An Investigation on Neural Spike Sorting Algorithms

2021 ◽  
Author(s):  
Helat A. Hussein ◽  
Subhi R. M. Zeebaree ◽  
Mohammed A. M. Sadeeq ◽  
Hanan M. Shukur ◽  
Ahmed Alkhayyat ◽  
...  
Keyword(s):  
Spike Sorting ◽  
Sorting Algorithms ◽  
Neural Spike

scholarly journals How many neurons can we see with current spike sorting algorithms?

2012 ◽  
Vol 211 (1) ◽  
pp. 58-65 ◽  
Author(s):  
Carlos Pedreira ◽  
Juan Martinez ◽  
Matias J. Ison ◽  
Rodrigo Quian Quiroga
Keyword(s):  
Spike Sorting ◽  
Sorting Algorithms

scholarly journals Evaluation of Spike Sorting Algorithms: Application to Human Subthalamic Nucleus Recordings and Simulations

Neuroscience ◽  
2019 ◽  
Vol 414 ◽  
pp. 168-185 ◽  
Author(s):  
Jeyathevy Sukiban ◽  
Nicole Voges ◽  
Till A. Dembek ◽  
Robin Pauli ◽  
Veerle Visser-Vandewalle ◽  
...  
Keyword(s):  
Subthalamic Nucleus ◽  
Spike Sorting ◽  
Sorting Algorithms

Performance comparison of extracellular spike sorting algorithms for single-channel recordings

2012 ◽  
Vol 203 (2) ◽  
pp. 369-376 ◽  
Author(s):  
Jiri Wild ◽  
Zoltan Prekopcsak ◽  
Tomas Sieger ◽  
Daniel Novak ◽  
Robert Jech
Keyword(s):  
Single Channel ◽  
Performance Comparison ◽  
Spike Sorting ◽  
Single Channel Recordings ◽  
Sorting Algorithms

scholarly journals SpikeForest, reproducible web-facing ground-truth validation of automated neural spike sorters

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Jeremy Magland ◽  
James J Jun ◽  
Elizabeth Lovero ◽  
Alexander J Morley ◽  
Cole Lincoln Hurwitz ◽  
...  
Keyword(s):  
Ground Truth ◽  
Brain Regions ◽  
Optimal Choice ◽  
Spike Sorting ◽  
Experimental Conditions ◽  
Total Size ◽  
Sorting Algorithms ◽  
Electrophysiological Recordings ◽  
Wide Range ◽  
Electrophysiological Studies

Spike sorting is a crucial step in electrophysiological studies of neuronal activity. While many spike sorting packages are available, there is little consensus about which are most accurate under different experimental conditions. SpikeForest is an open-source and reproducible software suite that benchmarks the performance of automated spike sorting algorithms across an extensive, curated database of ground-truth electrophysiological recordings, displaying results interactively on a continuously-updating website. With contributions from eleven laboratories, our database currently comprises 650 recordings (1.3 TB total size) with around 35,000 ground-truth units. These data include paired intracellular/extracellular recordings and state-of-the-art simulated recordings. Ten of the most popular spike sorting codes are wrapped in a Python package and evaluated on a compute cluster using an automated pipeline. SpikeForest documents community progress in automated spike sorting, and guides neuroscientists to an optimal choice of sorter and parameters for a wide range of probes and brain regions.

scholarly journals SHYBRID: A graphical tool for generating hybrid ground-truth spiking data for evaluating spike sorting performance

2019 ◽  
Author(s):  
Jasper Wouters ◽  
Fabian Kloosterman ◽  
Alexander Bertrand
Keyword(s):  
Ground Truth ◽  
Neural Recording ◽  
Data Driven ◽  
Sorting Algorithm ◽  
Spike Sorting ◽  
Recording Device ◽  
Ground Truth Data ◽  
Sorting Algorithms ◽  
Graphical Tool ◽  
Fine Tune

AbstractSpike sorting is the process of retrieving the spike times of individual neurons that are present in an extracellular neural recording. Over the last decades, many spike sorting algorithms have been published. In an effort to guide a user towards a specific spike sorting algorithm, given a specific recording setting (i.e., brain region and recording device), we provide an open-source graphical tool for the generation of hybrid ground-truth data in Python. Hybrid ground-truth data is a data-driven modelling paradigm in which spikes from a single unit are moved to a different location on the recording probe, thereby generating a virtual unit of which the spike times are known. The tool enables a user to efficiently generate hybrid ground-truth datasets and make informed decisions between spike sorting algorithms, fine-tune the algorithm parameters towards the used recording setting, or get a deeper understanding of those algorithms.

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