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Evaluation Batch #28 (PUBLIC)

Benchmark:
L5 tetrode, varying noise level
Description:
LTQFJJ <a href="http://pwdbohrvlefn.com/">pwdbohrvlefn</a>, [url=http://aijwmwhmwhvd.com/]aijwmwhmwhvd[/url], [link=http://tpfdvtmaglgp.com/]tpfdvtmaglgp[/link], http://zgclwapfbijg.com/
Algorithm:
Ground Truth
Author:
ehagen
Date Created:
Sept. 6, 2012

True positive: 100.00%

False positive: 0.00%

Benchmark:
L5 tetrode, varying noise level
Trial:
Raw, noise weight 0.1
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 0 0 0 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
1 1 611 517 94 611 517 94 0 0 0 0 0 0 0
2 2 483 398 85 483 398 85 0 0 0 0 0 0 0
3 3 811 674 137 811 674 137 0 0 0 0 0 0 0
4 4 524 418 106 524 418 106 0 0 0 0 0 0 0
5 5 659 554 105 659 554 105 0 0 0 0 0 0 0
6 6 250 199 51 250 199 51 0 0 0 0 0 0 0
7 7 311 262 49 311 262 49 0 0 0 0 0 0 0
8 8 475 404 71 475 404 71 0 0 0 0 0 0 0

Evaluation Plots

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.

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

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.

All spike waveforms superimposed.

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.

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: 100.00%

False positive: 0.00%

Benchmark:
L5 tetrode, varying noise level
Trial:
Raw, noise weight 0.2
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 0 0 0 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
1 1 608 510 98 608 510 98 0 0 0 0 0 0 0
2 2 482 384 98 482 384 98 0 0 0 0 0 0 0
3 3 858 709 149 858 709 149 0 0 0 0 0 0 0
4 4 556 440 116 556 440 116 0 0 0 0 0 0 0
5 5 674 569 105 674 569 105 0 0 0 0 0 0 0
6 6 254 199 55 254 199 55 0 0 0 0 0 0 0
7 7 346 285 61 346 285 61 0 0 0 0 0 0 0
8 8 523 441 82 523 441 82 0 0 0 0 0 0 0

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: 100.00%

False positive: 0.00%

Benchmark:
L5 tetrode, varying noise level
Trial:
Raw, noise weight 0.3
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 0 0 0 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
1 1 585 495 90 585 495 90 0 0 0 0 0 0 0
2 2 459 369 90 459 369 90 0 0 0 0 0 0 0
3 3 860 728 132 860 728 132 0 0 0 0 0 0 0
4 4 561 437 124 561 437 124 0 0 0 0 0 0 0
5 5 667 568 99 667 568 99 0 0 0 0 0 0 0
6 6 232 193 39 232 193 39 0 0 0 0 0 0 0
7 7 321 274 47 321 274 47 0 0 0 0 0 0 0
8 8 529 448 81 529 448 81 0 0 0 0 0 0 0

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: 100.00%

False positive: 0.00%

Benchmark:
L5 tetrode, varying noise level
Trial:
Raw, noise weight 0.4
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 0 0 0 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
1 1 593 493 100 593 493 100 0 0 0 0 0 0 0
2 2 456 366 90 456 366 90 0 0 0 0 0 0 0
3 3 808 684 124 808 684 124 0 0 0 0 0 0 0
4 4 527 426 101 527 426 101 0 0 0 0 0 0 0
5 5 633 537 96 633 537 96 0 0 0 0 0 0 0
6 6 237 199 38 237 199 38 0 0 0 0 0 0 0
7 7 314 258 56 314 258 56 0 0 0 0 0 0 0
8 8 464 390 74 464 390 74 0 0 0 0 0 0 0

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: 100.00%

False positive: 0.00%

Benchmark:
L5 tetrode, varying noise level
Trial:
Raw, noise weight 0.5
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 0 0 0 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
1 1 595 500 95 595 500 95 0 0 0 0 0 0 0
2 2 480 379 101 480 379 101 0 0 0 0 0 0 0
3 3 838 694 144 838 694 144 0 0 0 0 0 0 0
4 4 521 417 104 521 417 104 0 0 0 0 0 0 0
5 5 618 526 92 618 526 92 0 0 0 0 0 0 0
6 6 225 186 39 225 186 39 0 0 0 0 0 0 0
7 7 334 273 61 334 273 61 0 0 0 0 0 0 0
8 8 455 386 69 455 386 69 0 0 0 0 0 0 0

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.