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
- 18 A new synaptic modification algorithm and rhythmic oscillation
- 19 ‘Normal’ and ‘abnormal’ dynamic behaviour during synaptic transmission
- 20 Computer simulation studies to deduce the structure and function of the human brain
- 21 Access stability of cyclic modes in quasirandom networks of threshold neurons obeying a deterministic synchronous dynamics
- 22 Transition to cycling in neural networks
- 23 Exemplification of chaotic activity in non-linear neural networks obeying a deterministic dynamics in continuous time
- The cerebellum and the hippocampus
- Olfaction, vision and cognition
- Applications to experiment, communication and control
- Author index
- Subject index
18 - A new synaptic modification algorithm and rhythmic oscillation
from Cyclic phenomena and chaos in neural networks
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
- 18 A new synaptic modification algorithm and rhythmic oscillation
- 19 ‘Normal’ and ‘abnormal’ dynamic behaviour during synaptic transmission
- 20 Computer simulation studies to deduce the structure and function of the human brain
- 21 Access stability of cyclic modes in quasirandom networks of threshold neurons obeying a deterministic synchronous dynamics
- 22 Transition to cycling in neural networks
- 23 Exemplification of chaotic activity in non-linear neural networks obeying a deterministic dynamics in continuous time
- The cerebellum and the hippocampus
- Olfaction, vision and cognition
- Applications to experiment, communication and control
- Author index
- Subject index
Summary
Introduction
Rhythmic oscillation is a fundamental component which can be found in the various kinds of nervous systems (Friesen & Stent, 1977; Thompson, 1982). In a neural network with a ring-structured set of synaptic connections, a set of oscillations with different phases can be generated (Morishita & Yajima, 1972; Stein et al., 1974), and the occurrence of such rhythmic oscillation is also confirmed in different types of neural networks (Matsuoka, 1985). Since, however, various additional connections can cause a disturbance which easily extinguishes the rhythmic oscillation in the neural network, some function for maintaining the rhythmic oscillation should be expected to exist in the synapses if such signals play an important role in the nervous system.
A new synaptic modification algorithm is proposed which employs the average impulse density (AID) and the average membrane potential (AMP); examination of the effect of synaptic modification on rhythmic oscillation has been attempted (Tsutsumi & Matsumoto, 1984a). Simulation demonstrated some cases in which rhythmic oscillation reappears, by applying the algorithm to the disturbed ring neural network where the rhythmic oscillation was previously extinguished.
If that is the case, how can such oscillation derived from the neural network with feedback inhibition be processed in the following neural network with, for example, the feedforward system? Here we take, as an instance, the cerebellar circuitry including both feedback and feedforward systems, and discuss the relationship between synaptic modification and rhythmic oscillation in the neural network.
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- Computer Simulation in Brain Science , pp. 268 - 292Publisher: Cambridge University PressPrint publication year: 1988
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