BACK PROPAGATION TECHNIQUE-MODELING CORTICAL COMPUTATION

最近发现，使用以下参数训练的模型神经网络 反向传播过程进行类似大脑的计算， 发展出与大脑皮层非常相似的隐藏单元 神经元 一个特别明显的例子是我们最近的面积模型 猴后顶叶皮质7a。 响应特性 的模型隐藏单元紧密匹配的一类神经元 占了7a区的一半 经验观察 这种类型的建议，反向传播范式可能服务于 作为一种分析大脑皮层神经元损伤机制的通用技术， 计算 如果这是真的， 网络中的隐藏单元对应于许多 不同的皮层区域 本研究旨在 为了探索这一猜想，我们将建模工作扩展到 其他皮层区域和计算的变化。 我们尤其 将加强原来的7a区模型，使其处理三个- 三维表示，开发初级视觉皮层模型， 使用我们以前对方向和立体声的观察， 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.