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首页> 外文期刊>International Journal of Innovative Computing Information and Control >A BIO-INSPIRED COMPUTATIONAL NEURAL MODEL FOR ILLUSTRATION FACE AND CAR EXPERTISE EFFECT ON THE GATEWAY TO THE RIGHT FFA
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A BIO-INSPIRED COMPUTATIONAL NEURAL MODEL FOR ILLUSTRATION FACE AND CAR EXPERTISE EFFECT ON THE GATEWAY TO THE RIGHT FFA

机译：生物启发性的神经网络计算模型，用于正确的FFA网关的插图面部和汽车专家效果

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摘要

The human visual system consists of a hierarchy of multiple cortical areas and it has been reported that a cortical region in the fusiform gyrus called the Fusiform Face Area, FFA, responds much more strongly to faces than to any other class of stimulus. Recent studies have also revealed that objects of visual expertise activate the FFA more strongly than non-expertise stimuli, and it was argued that the right FFA is involved in expertise-specific rather than face-specific visual processing. According to these evidences, we propose a new biologically plausible computational model to illustrate face and car expertise effect on the gateway to the right FFA. In addition, there has been reported a difference in the onset latency of macaque inferotemporal neural responses. This latter case is also considered in the proposed model, where faces are recognized in the first layer and in the second layer, a discrimination task between cars and other objects is carried out.
机译：人类的视觉系统由多个皮质区域的层次结构组成，据报道，在梭状回中的一个皮质区域，称为Fusiform Face Area，FFA，对面部的反应比对任何其他刺激类别的反应要强得多。最近的研究还表明，视觉专业知识的对象比非专业知识的刺激更能激活FFA，并且有人认为，正确的FFA涉及专业知识而非面部的视觉处理。根据这些证据，我们提出了一种新的生物学上可行的计算模型，以说明面部和汽车专业知识对通往正确FFA门户的影响。此外，据报道猕猴下颞神经反应的发作潜伏期有所不同。在建议的模型中也考虑了后一种情况，其中在第一层和第二层中识别出人脸，然后执行汽车与其他物体之间的区分任务。

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