Publication Type:

Conference Paper


2014 International Conference on High Performance Computing and Applications (ICHPCA) (2014)



adaptive exponential leaky integrate and fire model, AdEx model, Analytical models, Artificial neural networks, behavioural conditions, Bioelectric phenomena, Brain, brain neurons, cerebellum granular layer, computational modeling, electrophysiological data, GPGPU, graphic processing units, Graphics, graphics processing units, large network models, Model Fitting, model function, n-dimensional space search, neuron computational modeling, Neurons, Neurophysiology, neuroscience, nonlinear fitting, Optimization, parameter optimisation, parameter optimization, particle swam optimisation, Particle Swam Optimization, particle swarm optimisation, PSO, search problems, search space, single neuron models, spiking neuron model, stochastic optimization technique, stochastic programming, swam intelligence, Swarm Intelligence, test predictions, Three-dimensional displays


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.

Cite this Research Publication

Manjusha Nair, Krishna S., Dr. Bipin G. Nair, and Diwakar, S., “Parameter optimization and nonlinear fitting for computational models in neuroscience on GPGPUs”, in 2014 International Conference on High Performance Computing and Applications (ICHPCA), 2014.

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