BACK PROPAGATION TECHNIQUE-MODELING CORTICAL COMPUTATION

最近发现，模型神经网络用来训练 经常进行类脑计算的反向传播过程 开发具有与皮质非常相似的属性的隐藏单位 神经元。一个特别明显的例子是我们最近的面积模型 7A为猴后顶叶皮质。响应属性 模型的隐藏单元与一类神经元的隐藏单元非常匹配 约占7a区单位的一半。经验观察 这表明反向传播范式可能会服务于 作为分析大脑皮层损伤机制的通用技术 计算。如果这是真的，就有可能制作模型 具有与许多神经元相对应的隐藏单元的网络 不同的皮质区域。本文提出的研究是经过精心设计的 要通过将我们的建模工作扩展到 其他大脑皮层区域的多样性和计算。尤其是我们 将增强原来的区域7a模式，因此它处理三个- 维度表征，开发初级视觉皮质模型 利用我们之前对方位和立体声的观察，以及 建立感觉-运动整合过程的模型 发生在顶叶。

It has recently been found that model neural networks trained with the back propagation procedure to do brain-like computations often develop hidden units with properties very similar to cortical neurons. A particularly clear example is our recent model of area 7a of monkey posterior parietal cortex. The response properties of the model hidden units match closely those of a class of neurons making up about half the units in area 7a. Empirical observations of this sort suggest that the back propagation paradigm might serve as a general technique for analyzing the mechanism of cortical computation. If this is true, it will be possible to make model networks with hidden units corresponding to neurons in many different cortical areas. The research proposed here is designed to explore this conjecture by extending our modeling efforts to a variety of other cortical areas and computation. In particular we will enhance the original area 7a model so it deals with three- dimensional representation, develop a primary visual cortex model using our previous observations on orientation and stereo, and build models of the sensory-motor integration processes thought to occur in the parietal lobe.