Artificial Neural Network Modelling: An Introduction

While scientists from different disciplines, such as neuroscience, medicine and high performance computing, eagerly attempt to understand how the human brain functioning happens, Knowledge Engineers in computing have been successful in making use of the brain models thus far discovered to introduce heuristics into computational algorithmic modelling. Gaining further understanding on human brain/nerve cell anatomy, structure, and how the human brain functions, is described to be significant especially, to devise treatments for presently described as incurable brain and nervous system related diseases, such as Alzheimer’s and epilepsy. Despite some major breakthroughs seen over the last few decades neuroanatomists and neurobiologists of the medical world are yet to understand how we humans think, learn and remember, and how our cognition and behaviour are linked. In this context, the chapter outlines the most recent human brain research initiatives following which early Artificial Neural Network (ANN) architectures, components, related terms and hybrids are elaborated.

[1]  Igor Aleksander,et al.  Neural computing architectures: the design of brain-like machines , 1989 .

[2]  Teuvo Kohonen,et al.  Visual Explorations in Finance , 1998 .

[3]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[4]  BART KOSKO,et al.  Bidirectional associative memories , 1988, IEEE Trans. Syst. Man Cybern..

[5]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Roman Borisyuk,et al.  Bifurcation analysis of a neural network model , 1992, Biological Cybernetics.

[7]  Jim Connor,et al.  I Compute--Therefore I Am. , 1981 .

[8]  Teuvo Kohonen,et al.  Self-organized formation of topologically correct feature maps , 2004, Biological Cybernetics.

[9]  Tom M. Mitchell,et al.  Machine Learning and Data Mining , 2012 .

[10]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[11]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[12]  Paul Smolensky,et al.  Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1990, Artif. Intell..

[13]  W. Pitts,et al.  How we know universals; the perception of auditory and visual forms. , 1947, The Bulletin of mathematical biophysics.

[14]  W. Freeman,et al.  Spatial EEG patterns, non-linear dynamics and perception: the neo-sherringtonian view , 1985, Brain Research Reviews.

[15]  Stephen Grossberg,et al.  A massively parallel architecture for a self-organizing neural pattern recognition machine , 1988, Comput. Vis. Graph. Image Process..

[16]  Bernard Widrow,et al.  Adaptive switching circuits , 1988 .

[17]  T. Kohonen.  Self-organized formation of topographically correct feature maps , 1982 .

[18]  T. Kohonen,et al.  Visual Explorations in Finance with Self-Organizing Maps , 1998 .

[19]  Bernard Widrow,et al.  Associative Storage and Retrieval of Digital Information in Networks of Adaptive “Neurons” , 1962 .

[20]  LiMin Fu,et al.  Knowledge discovery based on neural networks , 1999, Commun. ACM.

[21]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[22]  S.-I. Amari,et al.  Neural theory of association and concept-formation , 1977, Biological Cybernetics.

[23]  K. Kaneko.  Clustering, coding, switching, hierarchical ordering, and control in a network of chaotic elements , 1990 .

[24]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[25]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[26]  John Moody,et al.  Fast Learning in Networks of Locally-Tuned Processing Units , 1989, Neural Computation.