Valle-Lisboa Juan C, Reali Florencia, Anastasía Héctor, Mizraji Eduardo
Sección Biofísica, Facultad de Ciencias, Universidad de la República, Iguá 4225, Montevideo 11400, Uruguay.
Neural Netw. 2005 Sep;18(7):863-77. doi: 10.1016/j.neunet.2005.03.009.
The development of neural network models has greatly enhanced the comprehension of cognitive phenomena. Here, we show that models using multiplicative processing of inputs are both powerful and simple to train and understand. We believe they are valuable tools for cognitive explorations. Our model can be viewed as a subclass of networks built on sigma-pi units and we show how to derive the Kronecker product representation from the classical sigma-pi unit. We also show how the connectivity requirements of the Kronecker product can be relaxed considering statistical arguments. We use the multiplicative network to implement what we call an Elman topology, that is, a simple recurrent network (SRN) that supports aspects of language processing. As an application, we model the appearance of hallucinated voices after network damage, and show that we can reproduce results previously obtained with SRNs concerning the pathology of schizophrenia.
神经网络模型的发展极大地增进了对认知现象的理解。在此，我们表明使用输入乘法处理的模型既强大又易于训练和理解。我们认为它们是认知探索的宝贵工具。我们的模型可被视为基于西格玛-派单元构建的网络的一个子类，并且我们展示了如何从经典的西格玛-派单元推导出克罗内克积表示。我们还展示了考虑统计论据时如何放宽克罗内克积的连通性要求。我们使用乘法网络来实现我们所谓的埃尔曼拓扑结构，即一种支持语言处理方面的简单循环网络（SRN）。作为一个应用，我们对网络损伤后幻觉性声音的出现进行建模，并表明我们能够重现先前用SRN获得的关于精神分裂症病理学的结果。
