multilayer perceptron neural network (mlp) with bayesian regularization function  (MathWorks Inc)

Journal: PLoS Computational Biology

Article Title: Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb

doi: 10.1371/journal.pcbi.1004798

Figure Lengend Snippet: A. Response matrix showing normalized responses of AOB units responsive to at least one vaginal secretion stimulus (n = 92 units, stimulus set 1). Each row shows the normalized responses of one unit to the twelve stimuli indicated at the bottom. B-C. Pairwise discriminations. The six different comparisons are indicated by black lines in B, and the average classifier performance on those as a function of the number of units is shown in C. The gray double-headed arrows in B represent reciprocal tests of generalization, in which one classifier is tested with the data used to train the other, and vice versa. Average performance on the generalization tests is indicated by the gray traces in C. Plots are truncated at 30 units because further inclusion of units did not significantly improve performance in this analysis. D-E: Schematic and performance on dilution invariant classifications (orange), and generalization across strains (gray). F-G : Classification of reproductive state across dilutions and strains. Panels C, E, and G also show the best perceptron performance as a thick solid line, and the best single unit performance as a broken line. The “best classifier”, and “best single neuron” lines represent averages of the best performance obtained with each individual classifier within a category (there are six classifiers in the simple pairwise category in panels B-C, and two classifiers in the across-dilution category in panels D-E).

Article Snippet: Here, we used the perceptron function in MATLAB (neural networks toolbox, R2014a, http://www.mathworks.com/help/nnet/ref/perceptron.html ) to create a perceptron network.

Techniques:

Journal: PLoS Computational Biology

Article Title: Extracting Behaviorally Relevant Traits from Natural Stimuli: Benefits of Combinatorial Representations at the Accessory Olfactory Bulb

doi: 10.1371/journal.pcbi.1004798

Figure Lengend Snippet: Comparison of the performance of the best individual unit, best perceptron classifier, and best SVM classifier (left in each panel), and an analysis of the effects of unit removal on perceptron performance (right panel). Each panel shows results of 10 repeated unit removal cycles. In each stage of each cycle, the one unit with the highest absolute weight was removed. The dotted red line indicates the performance of the best individual unit. The vertical black line indicates the 10 cycle average of the number of units removed before perceptron performance drops below that obtained with the best individual unit. Values are given in the text. A. Reproductive state classifications with vaginal secretions. B. Strain classifications with vaginal secretions. C. Reproductive state classifications with urine. D. Strain classifications with urine. E. Reproductive state classifications across secretions. F. Strain classifications across secretions.

Article Snippet: Here, we used the perceptron function in MATLAB (neural networks toolbox, R2014a, http://www.mathworks.com/help/nnet/ref/perceptron.html ) to create a perceptron network.

Techniques: Comparison