LEARNING SIMPLE ARITHMETIC PROCEDURE

这项研究的长期目标是了解可能的 学习简单认知程序的神经机制 例如通过过程的神经网络建模的算法。 的 具体目标是： (1)实现并测试加法的神经网络模型。 (2)分析网络如何解决任务，并使用 尊重人类数据。 (3)对网络进行损伤并分析其行为 人类数据。 (4)为了确定网络再训练的最佳方法， 损害 加法的神经网络以前也有人做过，但都是针对单次加法的 数位加法 这里的区别是试图模拟 并行网络中的顺序加法过程。 该网络将 通过反向传播算法进行这项任务的训练。 我们将使用 循环网络，记录其状态下的处理历史 这样网络就可以使用这些信息来跟踪它 在手术中。 一旦网络学习了任务，标准 技术将被应用于分析网络如何解决 任务同样在这个阶段，随机病变可能会被引入到 网络，允许将其行为与在 人类 最后，网络将重新训练任务， 确定再培训的最佳方法。 这将有明显的 对治疗脑损伤患者的影响。 因此，为了探索被训练来完成这些任务的网络的行为， 希望学习更好的训练孩子和再训练损伤的方法 患者

The long term objective of this research is to understand the possible neural mechanisms underlying the learning of simple cognitive procedures such as arithmetic through neural network modeling of the process. The specific goals are: (1) To implement and test a neural network model of addition. (2) To analyze how the network has solved the task and evaluate it with respect to the human data. (3) To lesion the network and analyze its behavior with reference to human data. (4) To determine the optimal method of retraining the network after damage. Neural network of addition have been done before, but only for single digit addition. The difference here is the attempt to model the sequential process of addition in a parallel network. The network will be trained in this task via the back propagation algorithm. We will use recurrent networks that record their processing history in their state so that the network can use this information to keep track of where it is in the procedure. Once the network has learned the task, standard techniques will be applied to analyze how the network has solved the task. Also at this stage, random lesions may be introduced into the network, which allow comparison of its behavior with that observed in humans. Finally, the network will then be retrained on the task to determine the optimal method of retraining. This will have obvious implications for the treatment of brain damaged patients with acalculia. Thus exploring the behavior of networks trained to do these tasks, we hope to learn better ways of training children and retraining lesion patients.