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
19 - ‘Normal’ and ‘abnormal’ dynamic behaviour during synaptic transmission
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 behaviour is characteristic for the nervous system at different hierarchical levels. Periodic temporal patterns can be generated both by endogenous pacemaker neurons and by multicellular neural networks. At single-cell level it was demonstrated, both experimentally and theoretically, that periodic membrane potentials could bifurcate to more complex oscillatory behaviour (ultimately identified by chaos) in response to drug treatment (Holden, Winlow & Haydon, 1982; Chay, 1984). Even the alteration of periodically synchronized oscillation and chaotic behaviour has been found in periodically forced oscillators of squid giant axons (Aihara, Matsumoto & Ichikawa, 1985).
The appearance of quasi-periodicity and chaos has been associated with abnormal neural phenomena not only at single neural level but as well at macroscopic scale connecting chaotic EEG dynamics to epileptic seizure (Babloyantz, Salazar & Nicholis, 1985). At intermediate level, chaotic behaviour was found in a model of the central dopaminergic neuronal system, and was associated with schizophrenics (King, Barchas & Huberman, 1984).
‘Normal’ and ‘abnormal’ dynamic behaviour, also at intermediate, namely synaptic level, has recently been investigated (Érdi & Barna, 1986; Éedi & Barna, 1987). Preliminary numerical calculations suggested that the regular periodic operation of synaptic level rhythmic generator of cholinergic system requires a fine-tuned neurochemical control system. Even mild impairment of the metabolism might imply ‘abnormal’ dynamic synaptic activity.
Memory disorders associated with Alzheimer's disease, partially due to disturbance of the control system of acetylcholine (ACh) synthesis, can be accompanied by change of dynamic patterns of firing frequency (Wurtman, Hefti & Melamed, 1981).
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- Computer Simulation in Brain Science , pp. 293 - 302Publisher: Cambridge University PressPrint publication year: 1988
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