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Graph neural networks (GNNs) have achieved state-of-the- art performance in graph representation learning by exploit- ing statistical correlations. However, ... a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process. It is an intrinsic phenomenon unrelated to cardiac rhythm or neural and hormonal regulation. Vasomotion is found to be particularly prominent under conditions ... ABSTRACT. Introduction: Natural Language Processing through artificial neural networks has gaps that can be addressed by Information Science. ... information-processing system in order to have a mind. The answer is the ... neural network (see Shea, 2007). Nevertheless, we can ignore this set of ... Dec 2, 2021 ... Each phase involves aligning the connections of the neural network with patterns of information contained in the input dataset or preceding ... ... system can become highly synergistic; and (b) how informational states map onto artificial neural networks in various learning modes. Here we employ an ... Feb 26, 2021 ... We developed a context-aware graph neural network (GNN) model utilizing the pairwise relationship among participants to investigate the ... Cognitive Neuroscience 2018: On the biological (or social) expedience of the mechanism of information integration in neural networks of the brain through. The Informational Complexity of Learning: Perspectives on Neural Networks and Generative Grammar is a very interdisciplinary work. Anyone interested in the ...