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Background: Atrioventricular nodal reentrant tachycardia (AVNRT) sometimes recurs even after anatomical slow pathway (SP) ablation targeting the rightward inferior extension (RIE). This multicenter study aimed to determine the reasons for AVNRT recurrence. Methods and Results: Forty-six patients were treated successfully for recurrent AVNRT. Initial treatment was for 38 slow-fast AVNRTs, 3 fast-slow AVNRTs, 2 slow-slow AVNRTs, 2 slow-fast and fast-slow AVNRTs, and 1 noninducible AVNRT. All initial treatments were of RF application to the RIE; SP elimination was achieved in 11, dual AVN physiology was seen in 29, and AVNRT remained inducible in 5. The recurrent AVNRTs included 34 slow-fast AVNRTs, 6 fast-slow AVNRTs, 3 slow-slow AVNRTs, 2 slow-fast and fast-slow AVNRTs, and 1 slow-fast and slow-slow AVNRTs. Successful ablation site was within the RIE in 39 and left inferior extension in 7. In 30 of 39, the successful RIE site was in the same area or higher than that of the initial procedure. Conclusion: For a high majority (around 85%) of patients in whom AVNRT recurs after initial ablation success, the site of a second successful procedure will be within the RIE even though the RIE was originally targeted. Furthermore, a high majority (around 86%) of sites of successful ablation will be higher than those originally targeted. [ABSTRACT FROM AUTHOR]

In the neural network for pattern recognition, when the selectivity of the feature-extracting cell is lowered to enhance the generalizing power, a tendency is produced that patterns with similar shapes but belonging to different categories are confused. Because of this property, it has been difficult to en-hance the discriminating power to separate patterns with similar shapes without deteriorating the generalizing power. If patterns are observed, however, focusing on the features which differ between similar patterns, it will be possible to discriminate similar patterns. In the earlier neocognitron, the feature-extracting cell has uniform sensitivity within the receptive field. Then, it is impossible to discriminate patterns by emphasizing the local feature, which is located at a particular position in the receptive field. This paper proposes a method of learning, where the sensitivity of the feature-extracting cell is made variable depending on the position in the receptive field, and the sensitivity is adjusted by the error backpropagation. After the learning, the feature-extracting cell exhibits a high sensitivity at the position where similar patterns have different features. Thus it is made possible to classify similar patterns without deteriorating the generalizing power. [ABSTRACT FROM AUTHOR]