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Competition improves robustness against loss of information.

Kermani Kolankeh A, Teichmann M, Hamker FH - Front Comput Neurosci (2015)

Bottom Line: Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections.Also the kind of the competition mechanisms plays an important role in robustness.Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Chemnitz University of Technology Chemnitz, Germany.

ABSTRACT
A substantial number of works have aimed at modeling the receptive field properties of the primary visual cortex (V1). Their evaluation criterion is usually the similarity of the model response properties to the recorded responses from biological organisms. However, as several algorithms were able to demonstrate some degree of similarity to biological data based on the existing criteria, we focus on the robustness against loss of information in the form of occlusions as an additional constraint for better understanding the algorithmic level of early vision in the brain. We try to investigate the influence of competition mechanisms on the robustness. Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections. Each of those methods is known to be capable of developing receptive fields comparable to those of V1 simple-cells. Since measuring the robustness of methods having simple-cell like receptive fields against occlusion is difficult, we measure the robustness using the classification accuracy on the MNIST hand written digit dataset. For this we trained all methods on the training set of the MNIST hand written digits dataset and tested them on a MNIST test set with different levels of occlusions. We observe that methods which employ competitive mechanisms have higher robustness against loss of information. Also the kind of the competition mechanisms plays an important role in robustness. Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule.

No MeSH data available.


Related in: MedlinePlus

Classification accuracy on the output of FastICA, NMFSC, HNN, and PC/BC, using the occluded MNIST test set. Methods using competitive mechanisms show better robustness.
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Figure 5: Classification accuracy on the output of FastICA, NMFSC, HNN, and PC/BC, using the occluded MNIST test set. Methods using competitive mechanisms show better robustness.

Mentions: To investigate the differences in robustness to increasing levels of occlusions in the input, we have measured the classification accuracy of all methods and the raw data on the test set. We repeated the experiments 10 times with each algorithm under different starting conditions, i.e., randomly initialized weights. We do not show the error bars as they are zero for FastICA and NMFSC as they are deterministic and have been low for PC/BC and the HNN. We observed (Figure 5) that FastICA does not improve the classification accuracy to that of the raw data. NMFSC causes a super-linear decrease of classification accuracy with respect to the linear increase of occlusion. PC/BC shows the highest robustness against occlusion. The robustness of the HNN is higher than NMFSC and lower than PC/BC. The methods having more “advanced” competitive mechanisms perform better under increasing occlusions.


Competition improves robustness against loss of information.

Kermani Kolankeh A, Teichmann M, Hamker FH - Front Comput Neurosci (2015)

Classification accuracy on the output of FastICA, NMFSC, HNN, and PC/BC, using the occluded MNIST test set. Methods using competitive mechanisms show better robustness.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4373393&req=5

Figure 5: Classification accuracy on the output of FastICA, NMFSC, HNN, and PC/BC, using the occluded MNIST test set. Methods using competitive mechanisms show better robustness.
Mentions: To investigate the differences in robustness to increasing levels of occlusions in the input, we have measured the classification accuracy of all methods and the raw data on the test set. We repeated the experiments 10 times with each algorithm under different starting conditions, i.e., randomly initialized weights. We do not show the error bars as they are zero for FastICA and NMFSC as they are deterministic and have been low for PC/BC and the HNN. We observed (Figure 5) that FastICA does not improve the classification accuracy to that of the raw data. NMFSC causes a super-linear decrease of classification accuracy with respect to the linear increase of occlusion. PC/BC shows the highest robustness against occlusion. The robustness of the HNN is higher than NMFSC and lower than PC/BC. The methods having more “advanced” competitive mechanisms perform better under increasing occlusions.

Bottom Line: Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections.Also the kind of the competition mechanisms plays an important role in robustness.Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule.

View Article: PubMed Central - PubMed

Affiliation: Department of Computer Science, Chemnitz University of Technology Chemnitz, Germany.

ABSTRACT
A substantial number of works have aimed at modeling the receptive field properties of the primary visual cortex (V1). Their evaluation criterion is usually the similarity of the model response properties to the recorded responses from biological organisms. However, as several algorithms were able to demonstrate some degree of similarity to biological data based on the existing criteria, we focus on the robustness against loss of information in the form of occlusions as an additional constraint for better understanding the algorithmic level of early vision in the brain. We try to investigate the influence of competition mechanisms on the robustness. Therefore, we compared four methods employing different competition mechanisms, namely, independent component analysis, non-negative matrix factorization with sparseness constraint, predictive coding/biased competition, and a Hebbian neural network with lateral inhibitory connections. Each of those methods is known to be capable of developing receptive fields comparable to those of V1 simple-cells. Since measuring the robustness of methods having simple-cell like receptive fields against occlusion is difficult, we measure the robustness using the classification accuracy on the MNIST hand written digit dataset. For this we trained all methods on the training set of the MNIST hand written digits dataset and tested them on a MNIST test set with different levels of occlusions. We observe that methods which employ competitive mechanisms have higher robustness against loss of information. Also the kind of the competition mechanisms plays an important role in robustness. Global feedback inhibition as employed in predictive coding/biased competition has an advantage compared to local lateral inhibition learned by an anti-Hebb rule.

No MeSH data available.


Related in: MedlinePlus