Book contents
- Frontmatter
- Contents
- List of contributors
- Preface
- Neurons and neural networks: general principles
- Synaptic plasticity, topological and temporal features, and higher cortical processing
- 5 Neurons with hysteresis?
- 6 On models of short- and long-term memories
- 7 Topology, structure, and distance in quasirandom neural networks
- 8 A layered network model of sensory cortex
- 9 Computer simulation of networks of electrotonic neurons
- 10 A possible role for coherence in neural networks
- 11 Simulations of the trion model and the search for the code of higher cortical processing
- 12 AND–OR logic analogue of neuron networks
- 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
10 - A possible role for coherence in neural networks
from Synaptic plasticity, topological and temporal features, and higher cortical processing
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
- 5 Neurons with hysteresis?
- 6 On models of short- and long-term memories
- 7 Topology, structure, and distance in quasirandom neural networks
- 8 A layered network model of sensory cortex
- 9 Computer simulation of networks of electrotonic neurons
- 10 A possible role for coherence in neural networks
- 11 Simulations of the trion model and the search for the code of higher cortical processing
- 12 AND–OR logic analogue of neuron networks
- 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
Introduction
This article addresses a well-defined issue: does coherent firing of several neurons play a role in the function of the cerebral cortex? Its main purpose is to present the results of a computer simulation of a neural network in which the exact timing of impulses is indeed of paramount importance. And it is demonstrated that such a network would have potentially useful powers of discrimination and recall.
It is reasonably clear that the timing of the arrival of nerve impulses at a given neuron cannot be a matter of total indifference. The voltage across a neural membrane relaxes back towards its resting value, once a stimulus has been removed, so it is easy to envisage situations in which incoming impulses will fail to provoke a response unless they can act in unison by arriving simultaneously, or nearly so, at the somatic region. And there is a considerable corpus of evidence that the timing of incoming impulses is important. In the human auditory system, for example, small temporal offsets between impulse trains in the two cochlear nerves is exploited to locate sound sources, while the relative timing of impulses in the same nerve appears to be essential for the correct functioning of speech discrimination (Sachs, Voigt & Young, 1983). There is also evidence that the timing of sensory stimulation, down at the ten-millisecond level, is critically important for classical conditioning (Sutton & amp; Barto, 1981). And on the clinical side, one sees an extreme example of neuronal synchronization in the case of epilepsy, which apparently arises from mutual excitation between neurons (Traub & Wong, 1982).
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- Computer Simulation in Brain Science , pp. 164 - 188Publisher: Cambridge University PressPrint publication year: 1988
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