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• W2513247796 abstract "In this thesis, a method for evaluating the accuracy of mathematical neuron models is developed, with the objective to both improve the quality and the speed of parameter optimization. To evaluate the method, we used a conductance based model of the cuneate nucleus. The model behavior was evaluated against neurons recorded in vivo. A big challenge when modeling neurons is the timing of the action potentials, since the initiation is stochastic. Therefore, the model artificially inserts the spikes at the recorded spike times. The accuracy of the model is evaluated as its capacity to capture the time evolution of the membrane potential both following and preceding the spikes. The evaluation method uses the L2-norm when comparing a few important time windows of the recording and the model. The method was used to optimize the parameters of the model. Sensitivity analysis is used to learn which model parameters affect the model accuracy the most. The results from the optimization showed that the developed method well describes the quality of the neuron model for different parameters. This tool will make it easier to build models that capture the responsive properties of the membrane potential in neuronal in vivo recordings. (Less)" @default.
• W2513247796 created "2016-09-16" @default.
• W2513247796 creator A5023401204 @default.
• W2513247796 date "2016-01-01" @default.
• W2513247796 modified "2023-09-26" @default.
• W2513247796 title "Method to measure the quality of a neuron model compared to recorded data" @default.
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