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Neural network modelling of the influence of channelopathies on reflex visual attention

机译:神经网络模型对反射性视觉注意的影响

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This paper introduces a model of Emergent Visual Attention in presence of calcium channelopathy (EVAC). By modelling channelopathy, EVAC constitutes an effort towards identifying the possible causes of autism. The network structure embodies the dual pathways model of cortical processing of visual input, with reflex attention as an emergent property of neural interactions. EVAC extends existing work by introducing attention shift in a larger-scale network and applying a phenomenological model of channelopathy. In presence of a distractor, the channelopathic network's rate of failure to shift attention is lower than the control network's, but overall, the control network exhibits a lower classification error rate. The simulation results also show differences in task-relative reaction times between control and channelopathic networks. The attention shift timings inferred from the model are consistent with studies of attention shift in autistic children.
机译:本文介绍了钙通道病(EVAC)存在时的紧急视觉注意模型。通过对通道病进行建模,EVAC有助于确定自闭症的可能原因。网络结构体现了视觉输入的皮层处理的双通道模型,反射注意力是神经相互作用的一种新兴特性。 EVAC通过在较大规模的网络中引入注意力转移并应用渠道疾病的现象学模型来扩展现有工作。在存在干扰因素的情况下,通道病变网络转移注意力的失败率低于控制网络,但总的来说,控制网络表现出较低的分类错误率。仿真结果还显示了控制和通道病理网络之间相对任务反应时间的差异。从模型中得出的注意力转移时间与自闭症儿童的注意力转移研究一致。

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