A neural network model of lateralization during letter identification.
A neural network model of lateralization during letter identification.
复制标题
字母识别过程中偏侧化的神经网络模型。
DOI：
10.1162/089892999563300
复制
发表时间：
1999
期刊：
影响因子：
--
通讯作者：
Reggia,JA
中科院分区：
文献类型：
--
作者：
Shevtsova,N;Reggia,JA
目前尚不清楚认知功能和其他功能的大脑偏侧化的原因。为了研究功能偏侧化的一个方面，开发了一种用于简单视觉识别任务的双半球神经网络模型，该模型具有两条并行交互的信息处理路径。该模型基于有关神经连接、活动动力学和突触可塑性的普遍接受的概念。使用无监督（Hebbian）和监督（Widrow-Hoff）学习规则的组合来训练模型来识别在左视觉半视野、中央位置和右视觉半视野中作为输入刺激呈现的一小组字母。每个视觉半场投射到对侧半球，两个半球通过模拟胼胝体相互作用。针对各种潜在的不对称性，研究了每个单独的半球对输入刺激识别过程的贡献。结果表明，多重不对称可能会导致偏侧化。偏侧化发生在具有较大尺寸、较高兴奋性或较高学习率参数的一侧。它看起来更密集，具有强抑制性胼胝体连接，支持胼胝体发挥功能抑制作用的假设。该模型清楚地证明了偏侧化对不同半球参数的依赖性，并表明计算模型有助于更好地理解偏侧化出现的机制。
The causes of cerebral lateralization of cognitive and other functions are currently not well understood. To investigate one aspect of function lateralization, a bihemispheric neural network model for a simple visual identification task was developed that has two parallel interacting paths of information processing. The model is based on commonly accepted concepts concerning neural connectivity, activity dynamics, and synaptic plasticity. A combination of both unsupervised (Hebbian) and supervised (Widrow-Hoff) learning rules is used to train the model to identify a small set of letters presented as input stimuli in the left visual hemifield, in the central position, and in the right visual hemifield. Each visual hemifield projects onto the contralateral hemisphere, and the two hemispheres interact via a simulated corpus callosum. The contribution of each individual hemisphere to the process of input stimuli identification was studied for a variety of underlying asymmetries. The results indicate that multiple asymmetries may cause lateralization. Lateralization occurred toward the side having larger size, higher excitability, or higher learning rate parameters. It appeared more intensively with strong inhibitory callosal connections, supporting the hypothesis that the corpus callosum plays a functionally inhibitory role. The model demonstrates clearly the dependence of lateralization on different hemisphere parameters and suggests that computational models can be useful in better understanding the mechanisms underlying emergence of lateralization.