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• W1632366792 abstract "A significant obstacle in using phenomenological models of spiking neurons for large-scale simulations is the approximation of the optimal parameters for a type of neuron, given the available experimental data. Here we show a method for optimizing the parameters of such models, based on a combination of different frequency-current and voltage-current relations of a neuron as well as known physiological properties. We also present a python toolbox which uses NeMo spiking neural network simulator and provides a fast GPU-based implementation of our method. As a benchmark, our toolbox was used to fit Izhikevich equations to neurological data obtained from a cat's thalamic relay cell. Our resulting model was able to predict the firing patterns of known membrane potential traces of this neuron, although they were not explicitly defined during training. A further comparison between this neuron model and a previous approach, when both models are used in the simulation of a generic thalamic nucleus, revealed that the distribution of neuronal avalanches is significantly different and conforms better to power law-like distributions, thus increasing the likelihood of a critical regime and the biological plausibility of the simulation." @default.
• W1632366792 created "2016-06-24" @default.
• W1632366792 creator A5066982052 @default.
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• W1632366792 date "2015-07-01" @default.
• W1632366792 modified "2023-09-24" @default.
• W1632366792 title "GPU-based fast parameter optimization for phenomenological spiking neural models" @default.
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• W1632366792 doi "https://doi.org/10.1109/ijcnn.2015.7280668" @default.
• W1632366792 hasPublicationYear "2015" @default.
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