BRAIN. Broad Research in Artificial Intelligence and Neuroscience-New Ideas for Brain Modelling 4

This paper continues the research that considers a new cognitive model based strongly on the human brain. In particular, it considers the neural binding structure of an earlier paper. It also describes some new methods in the areas of image processing and behaviour simulation. The work is all based on earlier research by the author and the new additions are intended to fit in with the overall design. For image processing, a grid-like structure is used with ‘full linking’. Each cell in the classifier grid stores a list of all other cells it gets associated with and this is used as the learned image that new input is compared to. For the behaviour metric, a new prediction equation is suggested, as part of a simulation, that uses feedback and history to dynamically determine its course of action. While the new methods are from widely different topics, both can be compared with the binary-analog type of interface that is the main focus of the paper. It is suggested that the simplest of linking between a tree and ensemble can explain neural binding and variable signal strengths.

本文延续了此前围绕强依赖人脑的新型认知模型展开的研究工作，尤其针对此前一篇论文中提出的神经绑定（neural binding）结构进行了深入探讨。此外，本文还介绍了图像处理与行为模拟领域的若干新型方法。本项工作全部基于作者既往研究成果，新增内容均旨在契合整体设计框架。
在图像处理方向，本文采用了带有‘全连接’特性的类网格结构：分类器网格中的每个单元均会存储其关联的全部其他单元的列表，该列表将作为已学习得到的图像模板，用于与新输入数据进行比对。针对行为度量任务，本文提出了一种全新的预测方程，作为模拟系统的组成部分，该方程可借助反馈机制与历史数据动态规划行动路径。尽管这两类新型方法分属迥异的研究领域，但二者均可与本文核心关注的二进制-模拟混合型接口开展对比分析。本文提出，树模型与集成模型之间最简形式的连接机制，即可解释神经绑定与可变信号强度的相关特性。