A Robust Evolutionary Optimisation Approach for Parameterising a Neural Mass Model

Elham Zareian, Jun Chen, Basabdatta Sen Bhattacharya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, a robust optimisation approach is introduced for parameterising a thalamic neural mass model that simulates brain oscillations such as observed in electroencephalogram and local field potentials. In a previous work, the model was informed by physiological attributes of the Lateral Geniculate Nucleus in mammals and rodents; the synaptic connectivity parameters in the model were set manually by trial and error to oscillate within the alpha band (8–13 Hz). However, such manual techniques constrain modelling approaches involving a larger parameter space, for example towards exploring alternative parameter sets that may underlie similar brain states under different environmental conditions and owing to inter-individual differences. In this work, we implement a robust optimisation technique that is based on single-objective Genetic Algorithms, and incorporate newly devised objective and penalty functions for tackling the stochastic nature of the model input. Furthermore, a clustering algorithm is employed to identify robust and distinct parameter regions that will mimic spontaneous changes in thalamic circuit parameters under similar brain states due to environmental and inter-individual differences. The results from our study suggest that multiple robust and distinct parameter regions indeed exist, and the model shows consistent dominant frequency of oscillation within the alpha band corresponding to all of these identified parameter sets.
Original languageEnglish
Title of host publicationArtificial Neural Networks and Machine Learning – ICANN 2016
Subtitle of host publication25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II
EditorsAlessandro E. P. Villa, Paolo Masulli, Antonio Javier Pons Rivero
Place of PublicationSwitzerland
PublisherSpringer Nature
Number of pages10
ISBN (Electronic)9783319447810
ISBN (Print)9783319447803
Publication statusPublished - Sept 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Electronic)0302-9743


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