Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- 1 Some recent developments in the theory of neural networks
- 2 Representation of sensory information in self-organizing feature maps, and the relation of these maps to distributed memory networks
- 3 Excitable dendritic spine clusters: nonlinear synaptic processing
- 4 Vistas from tensor network theory: a horizon from reductionalistic neurophilosophy to the geometry of multi-unit recordings
- Synaptic plasticity, topological and temporal features, and higher cortical processing
- Spin glass models and cellular automata
- Cyclic phenomena and chaos in neural networks
- The cerebellum and the hippocampus
- Olfaction, vision and cognition
- Applications to experiment, communication and control
- Author index
- Subject index
4 - Vistas from tensor network theory: a horizon from reductionalistic neurophilosophy to the geometry of multi-unit recordings
from Neurons and neural networks: general principles
Published online by Cambridge University Press: 05 February 2012
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- 1 Some recent developments in the theory of neural networks
- 2 Representation of sensory information in self-organizing feature maps, and the relation of these maps to distributed memory networks
- 3 Excitable dendritic spine clusters: nonlinear synaptic processing
- 4 Vistas from tensor network theory: a horizon from reductionalistic neurophilosophy to the geometry of multi-unit recordings
- Synaptic plasticity, topological and temporal features, and higher cortical processing
- Spin glass models and cellular automata
- Cyclic phenomena and chaos in neural networks
- The cerebellum and the hippocampus
- Olfaction, vision and cognition
- Applications to experiment, communication and control
- Author index
- Subject index
Summary
The brain and the computer: a misleading metaphor in place of brain theory
Contrary to the philosophy of natural sciences, the brain has always been understood in terms of the most complex scientific technology of manmade organisms, for the simple reason of human vanity. Before and after the computer era, the brain was paraded in the clothing of hydraulic systems (in Descartes' times), and in the modern era as radio command centers, telephone switchboards, learn-matrices or feedback control amplifiers. Presently, it is fashionable to borrow terms of holograms, catastrophes or even spin glasses. Comparing brains to computers, however, has been by far the most important and most grossly misleading metaphor of all. Its importance has been twofold. First, the early post-war era was the first and last time in history that such analogy paved the way both to a model of the single neuron, the flip–flop binary element, cf. McCulloch & Pitts, 1943, and to a grand mathematical theory of the function of the entire brain (i.e., information processing and control by networks implementing Boolean algebra, cf. Shannon, 1948; Wiener, 1948). Second, the classical computer, the so-called von Neumann machine, provided neuroscience with not only a metaphor, but at the same time with a powerful working tool. This made computer simulation and modeling flourish in the brain sciences as well (cf. Pellionisz, 1979).
The basic misunderstanding inherent in the metaphor, nevertheless, left brain theory in an eclipse, although the creator of the computers was the first to point out (von Neumann, 1958) that these living- and non-living epitomes of complex organisms appear to operate on diametrically opposite structuro–functional principles.
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- Chapter
- Information
- Computer Simulation in Brain Science , pp. 44 - 73Publisher: Cambridge University PressPrint publication year: 1988
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