Titlebook: Emerging Trends in Neuro Engineering and Neural Computation; Asim Bhatti,Kendall H. Lee,Chee Peng Lim Book 2017 Springer Nature Singapore

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Capturing Cognition via EEG-Based Functional Brain Networksues to model and explore both the intricacies and dynamics of functional brain networks. Although unravelling the activity of the human brain remains circumscribed by technological and ethical constraints, complex network analysis of EEG data offers new ways to quantitatively characterize neuronal c

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Modelling of Tumour-Induced Angiogenesis Influenced by Haptotaxis-invasion and tumour-induced angiogenesis, as these promote each other and make the tumour develop faster, becoming more harmful for some subgroups of tumours. Since the proposed research on angiogenesis and the neural development systems share similar tasks of accomplishing and supporting complex n

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Development of a Co-evolutionary Radial Basis Function Neural Classifier by a ,-Random Opponents Topoach and then performs a local search that is initiated by a scaled conjugate backpropagation algorithm to fine-tune the solution. Results from a benchmark study show high effectiveness of the co-evolved model with a .-random opponents topology in constructing an accurate yet compact network structu

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A Modified Functional Link Neural Network for Data Classificationy, the genetic algorithm is used for both feature selection as well as weight tuning in the FLNN. An experimental study using benchmark problems is conducted to evaluate the efficacy of the modified FLNN. The empirical results indicate that even though the structure of the modified FLNN is simpler,

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Monotone Data Samples Do Not Always Generate Monotone Fuzzy If-Then Ruleseed to be preprocessed, before being used for FIS modeling. In short, it is imperative to develop methods for preprocessing non-monotone fuzzy rules from data, e.g., monotone fuzzy rules relabeling, or removing non-monotone fuzzy rules, is important (and is potentially necessary) during the course o

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2196-8861 ee main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologies/techniques in the medical domain in the treatment of neural disorders..978-981-13-5009-2978-981-10-3957-7Series ISSN 2196-8861 Series E-ISSN 2196-887X

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Book 2017e modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologies/techniques in the medical domain in the treatment of neural disorders..

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Olfactory Learning and Training Methodsld be employed as neural interfaces for stable signal recording and stimulation. We hope this work will provide a concise picture of the evolution and the progress of current neural interfaces technology, the development of which is still in progress.