Self-Organizing Maps as a Model of Brain Mechanisms Potentially Linked to Autism
Self-Organizing Maps as a Model of Brain Mechanisms Potentially Linked to Autism
复制标题
DOI：
10.1109/tnsre.2007.897031
复制
发表时间：
2007-06
影响因子：
4.9
通讯作者：
Gerardo Noriega
中科院分区：
文献类型：
--
作者：
Gerardo Noriega
人工神经网络在精神病理综合征研究中的应用具有巨大的潜力。获得性和发育障碍，包括自闭症，最近已经提出了几个计算模型。在本文中，我们使用自组织地图的框架来研究自闭症的几个方面，通过在生物学上合理的方式建模学习过程中的异常。然后，我们解释所产生的特征图与自闭症的特点。测量了操纵自组织地图的物理结构和大小的效果，并与自闭症儿童神经发育异常的一般特征进行了比较。我们发现刺激覆盖没有影响，但对地图展开的负面影响，依赖于异常的强度，但不是发病的时间。我们通过引入注意力函数的概念来分析感官问题，用于模拟超敏和低敏。通过一个模型，其中遥远的邻居被明确拒绝的问题，重点放在细节，而不是整体分析，我们表明该模型可能会导致改善覆盖的精细形状的区域或孤立的刺激，但较差的地图展开。最后，我们考虑了噪声通信信道对地图发展的影响，并显示了地图的覆盖和展开的强烈敏感性。
The application of artificial neural networks in the study of psychopathological syndromes has great potential. Several computational models of acquired and developmental disorders, including autism, have been proposed recently. In this paper, we use the framework of self-organizing maps to study several aspects of autism, by modeling abnormalities in the learning process in biologically plausible manners. We then interpret the resulting feature maps with reference to autistic characteristics. The effects of manipulating the physical structure and size of self-organizing maps were measured and compared with the general characteristics of neural growth abnormalities in autistic children. We find no effect on stimuli coverage, but a negative impact on map unfolding, dependant on the intensity of the abnormality, but not the time of onset. We analyze sensory issues by introducing the concept of attention functions, used to model hypersensitivities and hyposensitivities. The issue of focus on details rather than the whole is analyzed through a model in which distant neighbors are explicitly rejected; we show the model may lead to improved coverage of finely-shaped areas or isolated stimuli, but poorer map unfolding. Finally, we consider effects of noisy communication channels on the development of maps, and show a strong sensitivity of both coverage and unfolding of maps.