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SELECT ••• `benchmark_benchmark`.`id`, `benchmark_benchmark`.`date_created`, `benchmark_benchmark`.`added_by_id`, `benchmark_benchmark`.`name`, `benchmark_benchmark`.`description`, `benchmark_benchmark`.`state`, `benchmark_benchmark`.`parameter`, `benchmark_benchmark`.`gt_access`, `benchmark_benchmark`.`owner_id` FROM `benchmark_benchmark` WHERE NOT (`benchmark_benchmark`.`state` IN (10, 30)) ORDER BY `benchmark_benchmark`.`name` DESC LIMIT 8
Time
0.40 ms
Database
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ID DATE_CREATED ADDED_BY_ID NAME DESCRIPTION STATE PARAMETER GT_ACCESS OWNER_ID
3 2012-06-14 15:07:50 9 Quiroga2004 - Easy 2 This is the benchmark published with the paper Quiroga et. al. 2004. Downloaded from: http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting The ground truth was shifted by 35 samples since the original timestamps were too far in front of the peaks of the spikes. 20 Noise 20 9
2 2012-06-14 15:03:15 9 Quiroga2004 - Easy 1 This is the benchmark published with the paper Quiroga et. al. 2004. Downloaded from: http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting The ground truth was shifted by 35 samples since the original timestamps were too far in front of the peaks of the spikes. 20 Noise 20 9
5 2012-06-14 15:20:20 9 Quiroga2004 - Difficult 2 This is the benchmark published with the paper Quiroga et. al. 2004. Downloaded from: http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting The ground truth was shifted by 35 samples since the original timestamps were too far in front of the peaks of the spikes. 20 Noise 20 9
4 2012-06-14 15:16:29 9 Quiroga2004 - Difficult 1 This is the benchmark published with the paper Quiroga et. al. 2004. Downloaded from: http://www2.le.ac.uk/departments/engineering/research/bioengineering/neuroengineering-lab/spike-sorting The ground truth was shifted by 35 samples since the original timestamps were too far in front of the peaks of the spikes. 20 Noise 20 9
10 2012-09-07 15:24:17 13 L5 tetrode, varying unit count Model rat L5 tetrode recording generated using a forward model in LFPy, with realistic L5 pyramidal cell models with active dynamics and synaptic event times from a spiking network driving the cells. Sampling rate 32 kHz, contain 120 s of recordings for a tetrode geometry similar to a Thomas Recordings device. In this benchmark, the ability of the sorting algorithm to extract an increasing number of unique units in the recordings is tested 20 Unit count 10 13
9 2012-09-05 11:33:38 13 L5 tetrode, varying noise level Model rat L5 tetrode recording generated using a forward model in LFPy, with realistic L5 pyramidal cell models with active dynamics and synaptic event times from a spiking network driving the cells. Sampling rate 32 kHz, contain 120 s of recordings for a tetrode geometry similar to a Thomas Recordings device. In this benchmark, the ability of the algorithm to extract spikes at different noise levels is tested. The underlying population geometry is equal between trials, but network- and noise-realizations are different, so firing patterns are different of the post-synaptic cells. 20 Noise 10 13
7 2012-06-17 07:11:28 11 Hippocampus, tetrode, forward model rat Hippocampus recording generated using a forward model in LFPy, with realistic hippocampal morphologies with passive membranes and somatic playback of experimentally obtained voltage traces driving the cells. Sampling rate 20 kHz, contain 240 s of recordings for a tetrode geometry similar to a Thomas Recordings device 20 #neurons 10 13
13 2012-11-19 09:43:45 23 HD-MEA Salamander retina test 2 New test for HD-MEA testdata. The signals are driven more gently, as they might have acted somewhat unphysiologically earlier. The ground truth is made in a different manner, so that one should expect fewer false positives, but maybe more false negatives? 20 ID 20 23