Bayesian Optimisation for a Biologically Inspired Population Neural Network
This work addresses the challenge for neuroscientists and computational modelers of efficiently tuning complex neural network models to simulate brain rhythms, though it is incremental as it applies an existing optimization method to an existing network.
The researchers tackled the problem of tuning hyper-parameters in a biologically inspired neural network to simulate specific brain rhythms, using Bayesian Optimization to automatically find optimal 8-dimensional parameter sets that constrain output power spectral peaks within alpha, theta, and beta frequency bands, replacing a manual trial-and-error method limited to three parameters.
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.