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Evaluation Batch #40 (PUBLIC)

Benchmark:
Quiroga2004 - Difficult 1
Description:
JaNAy–THmeK YOU! You are a gifted photographer and we are so happy to have watched in you action–we could see the artists eye take over in many poses we would not have thought of doing~you are great!
Algorithm:
Wave_clus (2.0)
Author:
ffranke
Date Created:
Sept. 10, 2012

True positive: 86.58%

False positive: 0.00%

Benchmark:
Quiroga2004 - Difficult 1
Trial:
Difficult1_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
0 454 144 310 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 00003 1115 957 158 943 904 39 0 0 0 0 0 53 119
00002 00002 1113 950 163 964 899 65 0 0 0 0 0 51 98
00003 00001 1155 986 169 1022 946 76 0 0 0 0 0 40 93

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: 87.59%

False positive: 0.75%

Benchmark:
Quiroga2004 - Difficult 1
Trial:
Difficult1_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
12 414 175 239 14 2 12

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 00001 1164 1015 149 1007 965 42 1 0 0 1 10 49 97
00002 00002 1155 1004 151 1003 934 69 1 4 12 1 0 69 82
00003 00003 1129 966 163 1010 909 101 0 8 0 0 2 57 60

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: 85.31%

False positive: 3.34%

Benchmark:
Quiroga2004 - Difficult 1
Trial:
Difficult1_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
18 412 163 249 98 77 21

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 00003 1159 1003 156 931 900 31 20 5 0 33 13 70 112
00002 00001 1172 1015 157 1032 954 78 32 5 18 18 5 43 74
00003 00002 1141 977 164 999 901 98 25 11 0 26 3 50 63

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: 69.74%

False positive: 15.82%

Benchmark:
Quiroga2004 - Difficult 1
Trial:
Difficult1_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
20 513 272 241 520 468 52

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 00003 1136 980 156 739 702 37 140 7 0 143 20 135 99
00002 00001 1099 939 160 832 772 60 188 29 20 91 7 76 93
00003 00002 1179 1024 155 810 729 81 140 16 0 234 25 61 49

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.