Artificial neural networks are intelligent systems that have been successfully used for prediction in different medical fields. In this study, efficiency of neural networks for prediction of lupus nephritis in patients with systemic lupus erythematosus (SLE) was compared with a logistic regression model and clinicians' diagnosis. Overall accuracy, sensitivity and specificity of the optimal neural network were 68.69, 73.77 and 62.96%, respectively. Overall accuracy of neural network was greater than the other two methods (P-value< 0.05). The neural network was more specific in predicting lupus nephritis (P-value < 0.01), but there was no significant difference between sensitivities of the three methods. Sensitivities of all three methods were greater than their specificities. We concluded that neural networks are efficient in predicting lupus nephritis in SLE patients.
Thalamic neurons have an exclusive property named bursting response. Bursting response seems to have a critical role in producing saliency map and encoding conspicuity of locations during visual attention. Attention window is developed in thalamus due to its retinotopic organization. The global competitive network in thalamic reticular nucleus (NRT) determines which thalamic cells should produce bursting response. These cells are corresponded with attention window or attended location in the visual field. The computational procedure of bursting response is studied by the means of neurobiological modeling of thalamic neurons and their bimodal behavior (periodic bursting and periodic spiking patterns). The effect of NRT on thalamic relay neurons is considered in each mode of thalamic response. We also achieved the results of modeling through computer simulation of bimodal behavior in thalamic neurons.
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