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Evaluation Batch #55 (PUBLIC)

Benchmark:
Quiroga2004 - Difficult 2
Description:
av6BRc <a href="http://iirowhdchizr.com/">iirowhdchizr</a>, [url=http://hlyzosjqypom.com/]hlyzosjqypom[/url], [link=http://wpszzrfsitph.com/]wpszzrfsitph[/link], http://niephbtjoohb.com/
Algorithm:
EToS (3.3)
Author:
takekawa
Date Created:
Sept. 21, 2012

True positive: 93.61%

False positive: 4.34%

Benchmark:
Quiroga2004 - Difficult 2
Trial:
Difficult2_noise005
Task State:
Success
TOGGLE DETAILS

Evaluation Summary:

Detection Errors Classification Errors
False Positives False Negatives
Total Total Non-Overlaps Overlaps Total Non-Overlaps Overlaps
1 70 0 70 145 0 145

Evaluation Results:

GT Unit Found Unit Ground Truth Found Spikes of Unit False Positives False Negatives
(True Spikes) (True Positives) Other Spikes Noise Found by other Unit Not detected
Total N-O O Total N-O O N-O O FP N-O O N-O O
GT = Ground Truth, O = Overlaps, N-O = Non-Overlaps
4 0 0 0 0 0 0 0 144 1 0 0 0 0
00001 2 1120 967 153 1053 967 86 0 0 0 0 55 0 12
00002 3 1109 939 170 1036 939 97 0 1 0 0 49 0 24
00003 1 1135 958 177 1060 958 102 0 0 0 0 41 0 34

Evaluation Plots

For every neuron in the sorting a piece of data is cut around every of its spikes. This is done for every channel (for multielectrode data) individually. The plot shows all cut spike waveforms superimposed over each other (gray traces). Dashed lines indicate channel boundaries. Colored waveforms represent the average of all spike waveforms (the template) for each neuron.

All spike waveforms superimposed.

The projections of all spikes onto the first two principle components is shown. Colors indicate neuron identity. This plot gives an impression on how the clusters look like and how good their separation (in PCA space) is. To compute this plot principle component analysis (PCA) is run on all spike waveforms of the sorting. The projections of each waveform is computed on the first two principle components.

This is the same as the previous cluster plot but for PCs 3 and 4.

For each pair of neurons the projections of every spike of both neurons on the vector that connects the templates is shown. This plot is described in Pouzat et al. 2002 "Using noise signature to optimize spike-sorting and to assess neuronal classification quality" (fig. 3 and 6) but here, the noise covariance matrix is not taken into account. Colors indicate neuron identity. The plot gives an impression on how well each pair of clusters is separable. Note however, that the uploaded spike sorting was used to compute this plot, the true separability using the ground truth could be different.

The first second of the spike trains of the sorting are plotted. This plot can be used to see if the website interpreted the uploaded spike train file correctly. Also, if the spike sorter splitted one cluster incorrectly into two (e.g. due to waveform change over time) this is clearly visible in this plot.

True positive: 92.81%

False positive: 5.72%

Benchmark:
Quiroga2004 - Difficult 2
Trial:
Difficult2_noise01
Task State:
Success
TOGGLE DETAILS

Evaluation Summary:

Detection Errors Classification Errors
False Positives False Negatives
Total Total Non-Overlaps Overlaps Total Non-Overlaps Overlaps
0 51 0 51 198 76 122

Evaluation Results:

GT Unit Found Unit Ground Truth Found Spikes of Unit False Positives False Negatives
(True Spikes) (True Positives) Other Spikes Noise Found by other Unit Not detected
Total N-O O Total N-O O N-O O FP N-O O N-O O
GT = Ground Truth, O = Overlaps, N-O = Non-Overlaps
4 0 0 0 0 0 0 1 101 0 0 0 0 0
00001 2 1187 1060 127 1068 1007 61 22 2 0 53 52 0 14
00002 1 1136 1007 129 1069 985 84 52 16 0 22 34 0 11
00003 3 1139 998 141 1076 997 79 1 3 0 1 36 0 26

Evaluation Plots

For every neuron in the sorting a piece of data is cut around every of its spikes. This is done for every channel (for multielectrode data) individually. The plot shows all cut spike waveforms superimposed over each other (gray traces). Dashed lines indicate channel boundaries. Colored waveforms represent the average of all spike waveforms (the template) for each neuron.

All spike waveforms superimposed.

The projections of all spikes onto the first two principle components is shown. Colors indicate neuron identity. This plot gives an impression on how the clusters look like and how good their separation (in PCA space) is. To compute this plot principle component analysis (PCA) is run on all spike waveforms of the sorting. The projections of each waveform is computed on the first two principle components.

This is the same as the previous cluster plot but for PCs 3 and 4.

For each pair of neurons the projections of every spike of both neurons on the vector that connects the templates is shown. This plot is described in Pouzat et al. 2002 "Using noise signature to optimize spike-sorting and to assess neuronal classification quality" (fig. 3 and 6) but here, the noise covariance matrix is not taken into account. Colors indicate neuron identity. The plot gives an impression on how well each pair of clusters is separable. Note however, that the uploaded spike sorting was used to compute this plot, the true separability using the ground truth could be different.

The first second of the spike trains of the sorting are plotted. This plot can be used to see if the website interpreted the uploaded spike train file correctly. Also, if the spike sorter splitted one cluster incorrectly into two (e.g. due to waveform change over time) this is clearly visible in this plot.

True positive: 93.87%

False positive: 5.17%

Benchmark:
Quiroga2004 - Difficult 2
Trial:
Difficult2_noise015
Task State:
Success
TOGGLE DETAILS

Evaluation Summary:

Detection Errors Classification Errors
False Positives False Negatives
Total Total Non-Overlaps Overlaps Total Non-Overlaps Overlaps
19 52 0 52 159 71 88

Evaluation Results:

GT Unit Found Unit Ground Truth Found Spikes of Unit False Positives False Negatives
(True Spikes) (True Positives) Other Spikes Noise Found by other Unit Not detected
Total N-O O Total N-O O N-O O FP N-O O N-O O
GT = Ground Truth, O = Overlaps, N-O = Non-Overlaps
4 0 0 0 0 0 0 3 62 3 0 0 0 0
00001 2 1142 966 176 1056 930 126 33 16 0 36 41 0 9
00002 3 1113 952 161 1061 946 115 2 3 16 6 29 0 17
00003 1 1185 1029 156 1112 1000 112 33 7 0 29 18 0 26

Evaluation Plots

For every neuron in the sorting a piece of data is cut around every of its spikes. This is done for every channel (for multielectrode data) individually. The plot shows all cut spike waveforms superimposed over each other (gray traces). Dashed lines indicate channel boundaries. Colored waveforms represent the average of all spike waveforms (the template) for each neuron.

All spike waveforms superimposed.

The projections of all spikes onto the first two principle components is shown. Colors indicate neuron identity. This plot gives an impression on how the clusters look like and how good their separation (in PCA space) is. To compute this plot principle component analysis (PCA) is run on all spike waveforms of the sorting. The projections of each waveform is computed on the first two principle components.

This is the same as the previous cluster plot but for PCs 3 and 4.

For each pair of neurons the projections of every spike of both neurons on the vector that connects the templates is shown. This plot is described in Pouzat et al. 2002 "Using noise signature to optimize spike-sorting and to assess neuronal classification quality" (fig. 3 and 6) but here, the noise covariance matrix is not taken into account. Colors indicate neuron identity. The plot gives an impression on how well each pair of clusters is separable. Note however, that the uploaded spike sorting was used to compute this plot, the true separability using the ground truth could be different.

The first second of the spike trains of the sorting are plotted. This plot can be used to see if the website interpreted the uploaded spike train file correctly. Also, if the spike sorter splitted one cluster incorrectly into two (e.g. due to waveform change over time) this is clearly visible in this plot.

True positive: 94.27%

False positive: 6.33%

Benchmark:
Quiroga2004 - Difficult 2
Trial:
Difficult2_noise02
Task State:
Success
TOGGLE DETAILS

Evaluation Summary:

Detection Errors Classification Errors
False Positives False Negatives
Total Total Non-Overlaps Overlaps Total Non-Overlaps Overlaps
86 65 0 65 135 103 32

Evaluation Results:

GT Unit Found Unit Ground Truth Found Spikes of Unit False Positives False Negatives
(True Spikes) (True Positives) Other Spikes Noise Found by other Unit Not detected
Total N-O O Total N-O O N-O O FP N-O O N-O O
GT = Ground Truth, O = Overlaps, N-O = Non-Overlaps
00001 3 1151 989 162 1045 924 121 38 15 0 65 17 0 24
00002 1 1195 1035 160 1134 1009 125 26 8 86 26 12 0 23
00003 2 1147 989 158 1114 977 137 39 9 0 12 3 0 18

Evaluation Plots

For every neuron in the sorting a piece of data is cut around every of its spikes. This is done for every channel (for multielectrode data) individually. The plot shows all cut spike waveforms superimposed over each other (gray traces). Dashed lines indicate channel boundaries. Colored waveforms represent the average of all spike waveforms (the template) for each neuron.

All spike waveforms superimposed.

The projections of all spikes onto the first two principle components is shown. Colors indicate neuron identity. This plot gives an impression on how the clusters look like and how good their separation (in PCA space) is. To compute this plot principle component analysis (PCA) is run on all spike waveforms of the sorting. The projections of each waveform is computed on the first two principle components.

This is the same as the previous cluster plot but for PCs 3 and 4.

For each pair of neurons the projections of every spike of both neurons on the vector that connects the templates is shown. This plot is described in Pouzat et al. 2002 "Using noise signature to optimize spike-sorting and to assess neuronal classification quality" (fig. 3 and 6) but here, the noise covariance matrix is not taken into account. Colors indicate neuron identity. The plot gives an impression on how well each pair of clusters is separable. Note however, that the uploaded spike sorting was used to compute this plot, the true separability using the ground truth could be different.

The first second of the spike trains of the sorting are plotted. This plot can be used to see if the website interpreted the uploaded spike train file correctly. Also, if the spike sorter splitted one cluster incorrectly into two (e.g. due to waveform change over time) this is clearly visible in this plot.