Kinetic modelling of synaptic functions in the alpha rhythm neural mass model
AffiliationUniversity of Lincoln
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AbstractIn this work, we introduce the kinetic framework for modelling synaptic transmission in an existing neural mass model of the thalamocortical circuitry to study Electroencephalogram (EEG) slowing within the alpha frequency band (8–13 Hz), a hallmark of Alzheimer’s disease (AD). Ligand-gated excitatory and inhibitory synapses mediated by AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid) and GABAA (gamma-amino-butyric acid) receptors respectively are modelled. Our results show that the concentration of the GABA neurotransmitter acts as a bifurcation parameter, causing the model to switch from a limit cycle mode to a steady state. Further, the retino-geniculate pathway connectivity plays a significant role in modulating the power within the alpha band, thus conforming to research proposing ocular biomarkers in AD. Overall, kinetic modelling of synaptic transmission in neural mass models has enabled a more detailed investigation into the neural correlates underlying abnormal EEG in AD.
CitationBhattacharya B.S., Coyle D., Maguire L.P., Stewart J. (2012) 'Kinetic Modelling of Synaptic Functions in the Alpha Rhythm Neural Mass Model.' In Villa A.E.P., Duch W., Érdi P., Masulli F., Palm G. (eds) 'Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7552, pp. 642-652. Springer, Berlin, Heidelberg.
PublisherSpringer-Verlag Berlin Heidelberg
JournalProceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part I
Meetings and Proceedings
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