A functional model of some Parkinson's Disease symptoms using a Guided Propagation Network

This paper presents a computational model of Parkinson's Disease (PD) symptoms. Based on psychophysiological data, the underlying system (Guided Propagation Network) implements coincidence detection between internal flows and stimuli, and can be dynamically controlled for representing the actio...

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Bibliographic Details
Published inArtificial intelligence in medicine Vol. 14; no. 3; pp. 237 - 258
Main Authors Toffano-Nioche, Claire, Beroule, Dominique, Tassin, Jean-Pol
Format Journal Article
LanguageEnglish
Published Netherlands Elsevier B.V 01.11.1998
Subjects
Coincidence detection
Computational model
Computer Simulation
Humans
Models, Biological
Neural Networks (Computer)
Neuromodulation
Neurotransmitter Agents
Parkinson Disease - physiopathology
Parkinson's disease
Pattern generation
Parkinson's disease
Computational model
Neuromodulation
Pattern generation
Coincidence detection
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Summary:This paper presents a computational model of Parkinson's Disease (PD) symptoms. Based on psychophysiological data, the underlying system (Guided Propagation Network) implements coincidence detection between internal flows and stimuli, and can be dynamically controlled for representing the action of neuromodulators such as dopamine (DA). By modelling the DA deficit involved in PD through a decrease of response thresholds in the production modules of a GPN, four symptoms are observed in experiments carried out on a computer simulation, and then attributed to a lack of synchrony between ‘proprioceptive stimuli’ and internal flows: reduced intensity, increased rate, saccades and spontaneous repetitions.
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ISSN:0933-3657
1873-2860
DOI:10.1016/S0933-3657(98)00036-0