Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- 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
- 26 Neural computations and neural systems
- 27 Development of feature-analyzing cells and their columnar organization in a layered self-adaptive network
- 28 Reafferent stimulation: a mechanism for late vision and cognitive processes
- 29 Mathematical model and computer simulation of visual recognition in retina and tectum opticum of amphibians
- 30 Pattern recognition with modifiable neuronal interactions
- 31 Texture description in the time domain
- Applications to experiment, communication and control
- Author index
- Subject index
26 - Neural computations and neural systems
from Olfaction, vision and cognition
Published online by Cambridge University Press: 05 February 2012
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- 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
- 26 Neural computations and neural systems
- 27 Development of feature-analyzing cells and their columnar organization in a layered self-adaptive network
- 28 Reafferent stimulation: a mechanism for late vision and cognitive processes
- 29 Mathematical model and computer simulation of visual recognition in retina and tectum opticum of amphibians
- 30 Pattern recognition with modifiable neuronal interactions
- 31 Texture description in the time domain
- Applications to experiment, communication and control
- Author index
- Subject index
Summary
In deciding on a chapter for this book I had to choose between my interests in bringing ideas from biology to computational hardware, and in trying to bring computational ideas towards the biological wetware. I chose the direction of biological wetware, but it is instructive to begin with a few words about VLSI ‘neural’ networks.
There have been sizeable associative memories built out of the VLSI kinds of hardware. A first effort succeeded in building a 22 neuron circuit (1). A 54-neuron circuit with its 3000 connections has been made at Bell Laboratories (2) and a 512-neuron circuit with its 256000 intersections has been fabricated, but not yet made to work (3). An interesting and significant feature which emerges from looking at these chips is the degree to which, although the idea of an associative memory is very simple, a large fraction of the devices and area on the chip are doing things which are essential to the overall function of the chip, but which seem peripheral to the idea of associative memory. I will describe a similar facet in the biological case, in going from the abstract idea of an associative task toward a complete neural system. Much paraphernalia must be added and many changes made in order that an elementary associative memory can perform a biological task.
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- Chapter
- Information
- Computer Simulation in Brain Science , pp. 405 - 415Publisher: Cambridge University PressPrint publication year: 1988
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