  We have used Bayesian Optimisation (BO) to find hyper-parameters in an
existing biologically plausible population neural network. The 8-dimensional
optimal hyper-parameter combination should be such that the network dynamics
simulate the resting state alpha rhythm (8 - 13 Hz rhythms in brain signals).
Each combination of these eight hyper-parameters constitutes a 'datapoint' in
the parameter space. The best combination of these parameters leads to the
neural network's output power spectral peak being constraint within the alpha
band. Further, constraints were introduced to the BO algorithm based on
qualitative observation of the network output time series, so that high
amplitude pseudo-periodic oscillations are removed. Upon successful
implementation for alpha band, we further optimised the network to oscillate
within the theta (4 - 8 Hz) and beta (13 - 30 Hz) bands. The changing rhythms
in the model can now be studied using the identified optimal hyper-parameters
for the respective frequency bands. We have previously tuned parameters in the
existing neural network by the trial-and-error approach; however, due to time
and computational constraints, we could not vary more than three parameters at
once. The approach detailed here, allows an automatic hyper-parameter search,
producing reliable parameter sets for the network.
