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 Ad Ex 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.
<p class="0abstract">Anxiety is a cognitive, behavioural, and biological response that prepares the individual to handle the stresses and conflicts of everyday life. The excessive appearance of this biological response is diagnosed as an anxiety disorder, which is often associated with Autonomic dysfunction (ADy). ADy is difficult to study in clinics with very few parameters available. Detection of ADy may not be possible/difficult in anxiety disorder with the existing method. In this study, we built machine learning models to identify ADy in subjects with anxiety using properties extracted from ECG and respiratory signals. For each dataset, statistical and frequency domain features were estimated from ECG and respiratory signals. Supervised machine learning (ML) algorithms were used to classify the subjects. Out of 23 features estimated, 11 were found to be statistically significant for the classification. We segmented the signals into 5, 10, and 30 minutes intervals to build generalized models. To overcome data imbalance, ensemble techniques like boosting was used. The highest accuracy was obtained in the SVM, Random forest and Gradient Boosting classifiers (cross-validation accuracy of 82.2%, 81.64% and 79.06% and; AUC of 0.81, 0.76 and 0.84) for 10 and 30 minutes segmented datasets. Our results showed that the features extracted from the ECG signal are a good marker for diagnosing ADy in patients with anxiety disorder. Further, a deep neural network-based model can be implemented that may achieve better accuracy for classification provided with the cost of a large number of datasets and computation time.</p>
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