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Attention is drawn to the possibility that self-organizing biological neural networks could spontaneously acquire the capability to carry out sophisticated computations. In particular it is shown that the effective action governing the formation of synaptic connections in models of networks of feature detectors that encorporate Kohonen-like self-organization can spontaneously lead to structures that are topologically nontrivial in both a 2-dimensional and 4-dimensional sense. It is suggested that the appearance of biological neural structures with a nontrivial 4-dimensional topology is the fundamental organizational principle underlying the emergence of advanced cognitive capabilities.

Attention is drawn to the possibility that self-organizing biological neural networks could spontaneously acquire the capability to carry out sophisticated computations. In particular it is shown that the effective action governing the formation of synaptic connections in models of networks of feature detectors that encorporate Kohonen-like self-organization can spontaneously lead to structures that are topologically nontrivial in both a 2-dimensional and 4-dimensional sense. It is suggested that the appearance of biological neural structures with a nontrivial 4-dimensional topology is the fundamental organizational principle underlying the emergence of advanced cognitive capabilities.

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We investigate multispectral space image classification using the new artificial computational intelligence model called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of concurrent small modular self-organizing artificial neural networks. For comparison, we evaluate the performances of Bayes classifier. The implemented neural/statistical classifiers are evaluated using a LANDSAT TM image with 7 bands (multi-sensor data fusion) composed by a set of 7-dimensional pixels, out of which a subset contains labelled pixels, corresponding to seven thematic categories of Earth images taken from space. The best experimental result leads to the recognition rate of 95.29 %. The model has defence applications for Earth surveillance from space.

The objective of the present study is the development of an automated computerized system that will assist the early diagnosis of fetal hypoxia We demonstrate that it is possible to distinguish between healthy subjects and acidemic fetuses by way of wavelet transform analysis of the fetal heart rate recordings and fetal pulse oximetry (FSpO2). We focus on the values of the standard deviation of the wavelet components (up to scale index 5) and we apply Self-Organizing-Map in order to investigate the relationship between the fetal heart rate variability in different scales and FSpO2 (taking as a threshold for the FSpO2, the 30% level and considering the minimum value of FSpO2 during a 10-minute segment) for normal and acidemic fetuses during the second stage of labor, which can be used to discriminate acidemic fetuses from normal ones, A total accuracy of 91% has been achieved, enabling us to correctly classify all the normal cases (but one) as belonging in the normal group and all pathologic cases (but two) as belonging in the acidemic group, therefore providing a clinically significant measure for the discrimination of the different groups, Fetal pulse oximetry seems to be an important additional source of information.

Deep Learning A-Z™ Hands-On Artificial Neural Networks(12. Part 4 - Self Organizing Maps)

The objective of the present study is the development of an automated computerized system that will assist the early diagnosis of fetal hypoxia. We demonstrate that it is possible to distinguish between healthy subjects and acidemic fetuses by way of wavelet transform analysis of the fetal heart rate recordings and fetal pulse oximetry (FSpO2). We focus on the values of the standard deviation of the wavelet components (up to scale index 5) and we apply Self-Organizing-Map in order to investigate the relationship between the fetal heart rate variability in different scales and FSpO2 (taking as a threshold for the FSpO2, the 30% level and considering the minimum value of FSpO2 during a 10-minute segment) for normal and acidemic fetuses during the second stage of labor, which can be used to discriminate acidemic fetuses from normal ones. A total accuracy of 91% has been achieved, enabling us to correctly classify all the normal cases (but one) as belonging in the normal group and all pathologic cases (but two) as belonging in the acidemia group, therefore providing a clinically significant measure for the discrimination of the different groups. Fetal pulse oximetry seems to be an important additional source of information.