Abstract:
Parameterization of computationally expensive forward models of the brain is a novel research area that has only recently become tractable due to supercomputing resources...Show MoreMetadata
Abstract:
Parameterization of computationally expensive forward models of the brain is a novel research area that has only recently become tractable due to supercomputing resources. However, there is a lack of examples demonstrating how to achieve accurate estimates of neural, synaptic, and connectivity parameters of large-scale brain simulations within a reasonable period of time. We present the novel application of MT-DREAMZ, an existing parallel differential evolution Markov chain monte carlo (MCMC) method, to estimate the parameters of a complex spiking neuron circuit simulation in a short period of time. The parameters are estimated so that the neural simulations match empirical data collected from a midbrain visual/attention region called the superior colliculus. The results of the parameter sweeps reveal several regions of parameter space that fit the empirical data. The highest likelihood parameter regions show regularities consistent with anatomical properties of the original brain region, such as the wide horizontal inhibitory neurons. Our results demonstrate that evolutionary statistical techniques are highly effective tools for investigating complex models of the brain due to their efficiency and parallelizability. Not only do the sweeps find good fits for an incredibly complex non-linear problem, but the resulting posterior likelihood distributions show patterns that fit independently obtained data from anatomical and physiological studies of the same brain region that were not included in the fitness function. This demonstrates that hybrid evolutionary methods can be applied to increase our understanding of the underlying structural and dynamic properties of the brain using only reasonable amounts of behavioral data.
Published in: 2015 IEEE Congress on Evolutionary Computation (CEC)
Date of Conference: 25-28 May 2015
Date Added to IEEE Xplore: 14 September 2015
ISBN Information: