Basic research on brain-inspired information processing systems with integrated computation for recognition and decision making

本研究旨在发展一种新的大脑计算理论，该理论整合了两种大脑功能，即识别（输入相关计算）和决策（输出相关计算）。我们还试图建立一个基于理论的基本数学模型，这可能有助于实现像大脑这样的高度智能信息处理系统。具体来说，我们以统一的方式将识别和决策相关的计算建模为动态贝叶斯网络模型上的概率推理。我们还推导出了概率推理问题的精确解，并提出了一种近似方法。

This research aimed for the development of a new computational theory of the brain that integrates two types of brain's functionality, that is, recognition (input-related computation) and decision making (output-related computation). We also tried to establish a fundamental mathematical model based on the theory that may contribute to the realization of highly intelligent information processing systems like the brain.Specifically, we have modeled the computation associated with recognition and decision making as probabilistic inference on a dynamic Bayesian network model in a unified manner. We have also derived the exact solution of the probabilistic inference problem and proposed an approximation method for it.