Fig. 10. Classification accuracies for (a) IRIS and (b) WBC data sets against the con Fig. 12. Classification accuracies for (a) IRIS and (b) WBC data sets against the number of Gaussian receptive fields. Fig. 11. Classification accuracies for (a) IRIS and (b) WBC data sets against the threshold value for merging neurons. Fig. 13. Classification accuracies against (a) and (b) mod values using τ Gaussian population encoding. Effect of the Pruning Threshold (Th_sim) and when it is <0.4 in the case of the WBC data set. The changes in classification performance with respect to the threshold value (Th_sim) for merging neurons are shown Effect of the Number of Receptive Gaussian in Fig. 11(a) and (b) for IRIS and WBC data sets, respectively. Fields/Square Cosines (q) For both the SpikeTemp and the rank-order methods, the The changes in classification performance with respect to results using Gaussian population encoding shown in Fig. 11 the number of Gaussian receptive fields (q value) are shown in indicate that the accuracy for both training and testing data Fig. 12(a) and (b) for IRIS and WBC data sets, respectively. sets is maintained above 95% when Th_sim is greater than 0.5 The results using Gaussian population encoding indicate for the IRIS data set, and when it is greater than 0.4 for the that the best performance is obtained when the number of WBC data set. A smaller threshold value increases the Gaussian receptive fields is set to 30 for the IRIS data set, likelihood of an added output neuron that should not be and is set to 15 for the WBC data set for both the SpikeTemp merged being merged, the classification accuracies degrade and the rank-order methods. From Fig. 12(a), we can see that gradually when the threshold value is <0.5 in the case of the for IRIS data set, the accuracies for training data sets are IRIS data set
Fig. 10. Classification accuracies for (a) IRIS and (b) WBC data sets against the con Fig. 11. Classification accuracies for (a) IRIS and (b) WBC data sets against the threshold value for merging neurons. Effect of the Pruning Threshold (Th_sim) The changes in classification performance with respect to the threshold value (Th_sim) for merging neurons are shown in Fig. 11(a) and (b) for IRIS and WBC data sets, respectively. For both the SpikeTemp and the rank-order methods, the results using Gaussian population encoding shown in Fig. 11 indicate that the accuracy for both training and testing data sets is maintained above 95% when Th_sim is greater than 0.5 for the IRIS data set, and when it is greater than 0.4 for the WBC data set. A smaller threshold value increases the likelihood of an added output neuron that should not be merged being merged, the classification accuracies degrade gradually when the threshold value is <0.5 in the case of the IRIS data set Fig. 12. Classification accuracies for (a) IRIS and (b) WBC data sets against the number of Gaussian receptive fields. Fig. 13. Classification accuracies against (a) τ and (b) mod values using Gaussian population encoding. and when it is <0.4 in the case of the WBC data set. Effect of the Number of Receptive Gaussian Fields/Square Cosines (q) The changes in classification performance with respect to the number of Gaussian receptive fields (q value) are shown in Fig. 12(a) and (b) for IRIS and WBC data sets, respectively. The results using Gaussian population encoding indicate that the best performance is obtained when the number of Gaussian receptive fields is set to 30 for the IRIS data set, and is set to 15 for the WBC data set for both the SpikeTemp and the rank-order methods. From Fig. 12(a), we can see that for IRIS data set, the accuracies for training data sets are
SpikeTemp%20.docx