Parameter optimization and nonlinear fitting for computational models in neuroscience on GPGPUs

Abstract

One of the main challenges in computational modeling of neurons is to reproduce the realistic behaviour of the neurons of the brain under different behavioural conditions. Fitting electrophysiological data to computational models is required to validate model function and test predictions. Various tools and algorithms exist to fit the spike train recorded from neurons to computational models. All these require huge computational power and time to produce biologically feasible results. Large network models rely on the single neuron models to reproduce population activity. A stochastic optimization technique called Particle Swam Optimisation (PSO) was used here to fit spiking neuron model called Adaptive Exponential Leaky Integrate and Fire (AdEx) model to the firing patterns of different types of neurons in the granular layer of the cerebellum. Tuning a network of different types of spiking neurons is computationally intensive, and hence we used Graphic Processing Units (GPU) to run the parameter optimisation of AdEx using PSO. Using the basic principles of swam intelligence, we could optimize the n-dimensional space search of the parameters of the spiking neuron model. The results were significant and we observed a 15X performance in GPU when compared to CPU. We analysed the accuracy of the optimization process with the increase in width of the search space and tuned the PSO algorithm to suit the particular problem domain. This work has promising roles towards applied modeling and can be extended to many other disciplines of model-based predictions.