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Models of neural networks
Computer Physics Communications 70 (1992) 219—220 North-Holland
Computer Physics Communications
Book review
Models of Neural Networks
E. Domany, J.L. van Hemmen and K. Schulten, eds., Springer-Verlag, Berlin, 1991. 345 + xvi pages, 78 figures. Hardcover price DM78.00. ISBN 3 540 51109 1.
“Models of Neural Networks” is a collection of papers reviewing up to date research in physicists’ neural networks, the firts in a new series. The editors state in the foreword that they intend this series “to be aimed at the level of beginning graduate students, that is, to be accessible to a wide readership”. Unfortunately, the papers in this volume are mostly approachable only by a person already possessing a wide knowledge of the concepts involved, that is someone who is already a researcher in the field. The basic problem that the authors encounter, is that it is impossible both to explain the results of the latest research, and at the same time keep the level to that of the new graduate. This is especially true for a subject relying so much on complicated mathematics. For the most part, they give up the struggle, and instead concentrate on communicating their results. This problem is typified by the first chapter, written by Van Hemmen and Kuhn. Despite the aim that it “may by read as a self-contained introduction to the theory of neural networks”, not once is it explained what is meant by the term “neuron”, formal or otherwise. The authors simply dive in, assuming that the reader is already familiar with these terms and the work of Hopfield and others. This is not helped by the confusing system of numbering and referring to equations, unique to this chapter. Any new graduate familiar enough with the field of neural networks to understand these terms, will presumably come from a computer science or engineering background, and will be unlikely to be able to understand the physics; conversely a new graduate with a physics background will not be familiar with the neural networks. Having said that the book fails in its stated aim, it does provide a good and up to date review of neural network research within the physics community. Physicists tend to concentrate on homogeneous systems of neurons that exhibit interesting collective behaviour. Such neural network models are similar to magnetic and spin-glass models that have established theoretical backgrounds. In this, they are somewhat separated from the mainstream neural networks community, which has tended to concentrate on non-homogeneous and layered networks. The two chapters which do treat such networks, by Ritter et al. on self-organizing maps, and by Domany and Meir on layered networks, treat these subjects with a mathematical slant and rigour not usually found in mainstream neural networks papers. In the final analysis, “Models of Neural Networks” fails in its stated aim of providing a review of the field that is accessible to a recent graduate, unless that graduate has come armed with a more basic primer on the principles involved. On the other hand, it does provide an up to date review of advances in the field of homogeneous neural networks for someone who has worked in the field and spent some time 0010-4655/92/$05.00 © 1992
—
Elsevier Science Publishers By. All rights reserved
220
1300k rclieh
away, or for the new graduate who has familiarised him or herself with the field in general, and wishes to make a more in depth study. It is not the content of the papers that is the problem it is just that the book is pitched at a deeper level than the editors say that they intended. —
R. Debenham Department of Engineering, University of Cambridge
Computer Physics Communications
Book review
Models of Neural Networks
E. Domany, J.L. van Hemmen and K. Schulten, eds., Springer-Verlag, Berlin, 1991. 345 + xvi pages, 78 figures. Hardcover price DM78.00. ISBN 3 540 51109 1.
“Models of Neural Networks” is a collection of papers reviewing up to date research in physicists’ neural networks, the firts in a new series. The editors state in the foreword that they intend this series “to be aimed at the level of beginning graduate students, that is, to be accessible to a wide readership”. Unfortunately, the papers in this volume are mostly approachable only by a person already possessing a wide knowledge of the concepts involved, that is someone who is already a researcher in the field. The basic problem that the authors encounter, is that it is impossible both to explain the results of the latest research, and at the same time keep the level to that of the new graduate. This is especially true for a subject relying so much on complicated mathematics. For the most part, they give up the struggle, and instead concentrate on communicating their results. This problem is typified by the first chapter, written by Van Hemmen and Kuhn. Despite the aim that it “may by read as a self-contained introduction to the theory of neural networks”, not once is it explained what is meant by the term “neuron”, formal or otherwise. The authors simply dive in, assuming that the reader is already familiar with these terms and the work of Hopfield and others. This is not helped by the confusing system of numbering and referring to equations, unique to this chapter. Any new graduate familiar enough with the field of neural networks to understand these terms, will presumably come from a computer science or engineering background, and will be unlikely to be able to understand the physics; conversely a new graduate with a physics background will not be familiar with the neural networks. Having said that the book fails in its stated aim, it does provide a good and up to date review of neural network research within the physics community. Physicists tend to concentrate on homogeneous systems of neurons that exhibit interesting collective behaviour. Such neural network models are similar to magnetic and spin-glass models that have established theoretical backgrounds. In this, they are somewhat separated from the mainstream neural networks community, which has tended to concentrate on non-homogeneous and layered networks. The two chapters which do treat such networks, by Ritter et al. on self-organizing maps, and by Domany and Meir on layered networks, treat these subjects with a mathematical slant and rigour not usually found in mainstream neural networks papers. In the final analysis, “Models of Neural Networks” fails in its stated aim of providing a review of the field that is accessible to a recent graduate, unless that graduate has come armed with a more basic primer on the principles involved. On the other hand, it does provide an up to date review of advances in the field of homogeneous neural networks for someone who has worked in the field and spent some time 0010-4655/92/$05.00 © 1992
—
Elsevier Science Publishers By. All rights reserved
220
1300k rclieh
away, or for the new graduate who has familiarised him or herself with the field in general, and wishes to make a more in depth study. It is not the content of the papers that is the problem it is just that the book is pitched at a deeper level than the editors say that they intended. —
R. Debenham Department of Engineering, University of Cambridge