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Evaluation Batch #38 (PUBLIC)

Benchmark:
Quiroga2004 - Easy 1
Description:
iwnIBb <a href="http://hndrwzgkqtko.com/">hndrwzgkqtko</a>, [url=http://gcymdrdgbqzu.com/]gcymdrdgbqzu[/url], [link=http://atdvyhificjk.com/]atdvyhificjk[/link], http://zbaxeexmutee.com/
Algorithm:
Wave_clus (2.0)
Author:
ffranke
Date Created:
Sept. 10, 2012

True positive: 83.75%

False positive: 0.00%

Benchmark:
Quiroga2004 - Easy 1
Trial:
Easy1_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
0 571 155 416 0 0 0

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 00002 1165 1005 160 985 957 28 0 0 0 0 0 48 132
00002 00003 1157 979 178 936 928 8 0 0 0 0 0 51 170
00003 00001 1192 1033 159 1022 977 45 0 0 0 0 0 56 114

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: 88.39%

False positive: 0.40%

Benchmark:
Quiroga2004 - Easy 1
Trial:
Easy1_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
14 409 151 258 0 0 0

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 00002 1151 1010 141 1015 960 55 0 0 12 0 0 50 86
00002 00003 1134 982 152 942 933 9 0 0 2 0 0 49 143
00003 00001 1237 1086 151 1156 1034 122 0 0 0 0 0 52 29

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: 85.53%

False positive: 0.52%

Benchmark:
Quiroga2004 - Easy 1
Trial:
Easy1_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
16 501 227 274 2 0 2

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 00002 1132 979 153 999 932 67 0 1 8 0 1 47 85
00002 00003 1188 1012 176 890 876 14 0 0 8 0 1 136 161
00003 00001 1157 983 174 1085 939 146 0 1 0 0 0 44 28

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: 72.63%

False positive: 0.69%

Benchmark:
Quiroga2004 - Easy 1
Trial:
Easy1_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
20 947 700 247 4 1 3

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 00002 1198 1036 162 962 897 65 0 0 0 1 3 138 94
00002 00003 1128 989 139 487 468 19 1 1 2 0 0 521 120
00003 00001 1148 997 151 1074 956 118 0 2 18 0 0 41 33

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.