Book contents
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 The biology of neural networks: a few features for the sake of non-biologists
- 3 The dynamics of neural networks: a stochastic approach
- 4 Hebbian models of associative memory
- 5 Temporal sequences of patterns
- 6 The problem of learning in neural networks
- 7 Learning dynamics in ‘visible’ neural networks
- 8 Solving the problem of credit assignment
- 9 Self-organization
- 10 Neurocomputation
- 11 Neurocomputers
- 12 A critical view of the modeling of neural networks
- References
- Index
2 - The biology of neural networks: a few features for the sake of non-biologists
Published online by Cambridge University Press: 30 November 2009
- Frontmatter
- Contents
- Preface
- Acknowledgments
- 1 Introduction
- 2 The biology of neural networks: a few features for the sake of non-biologists
- 3 The dynamics of neural networks: a stochastic approach
- 4 Hebbian models of associative memory
- 5 Temporal sequences of patterns
- 6 The problem of learning in neural networks
- 7 Learning dynamics in ‘visible’ neural networks
- 8 Solving the problem of credit assignment
- 9 Self-organization
- 10 Neurocomputation
- 11 Neurocomputers
- 12 A critical view of the modeling of neural networks
- References
- Index
Summary
This chapter is not a course on neurobiology. As stated in the title, it is intended to gather a few facts relevant to neural modeling, for the benefit of those not acquainted with biology. The material which is displayed has been selected on the following accounts. First of all, it is made of neurobiological data that form the basic bricks of the model. Then it comprises a number of observations which have been subjects for theoretical investigations. Finally, it strives to settle the limits of the current status of research in this field by giving an insight on the huge complexity of central nervous systems.
Three approaches to the study of the functioning of central nervous systems
Let us assume that we have a very complicated machine of unknown origin and that our goal is to understand its functioning. Probably the first thing we do is to observe its structure. In general this analysis reveals a hierarchical organization comprising a number of levels of decreasing complexity: units belonging to a given rank are made of simpler units of lower rank and so on, till we arrive at the last level of the hierarchy, which is a collection of indivisible parts.
The next step is to bring to light what the units are made for, how their presence manifests itself in the machine and how their absence damages its properties. This study is first carried out on pieces of the lowest order, because the functions of these components are bound to be simpler than those of items of higher rank.
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- Information
- An Introduction to the Modeling of Neural Networks , pp. 13 - 56Publisher: Cambridge University PressPrint publication year: 1992