论文标题
一种新型的高清计算代数：状态的非缔合性叠加，形成稀疏的束代表订单信息
A novel HD Computing Algebra: Non-associative superposition of states creating sparse bundles representing order information
论文作者
论文摘要
信息流入计算系统是通过一系列信息项。认知计算，即沿该顺序进行转换，需要表示项目信息以及顺序信息。最基本的操作之一是捆绑，即添加项目，导致“内存状态”，即捆绑，可以从中检索信息。如果使用的捆绑操作是关联的，例如普通的矢量添加，如果不施加其他代数结构，就无法表示顺序信息。由神经元活动的随机总和启发的一个简单的随机二进制捆绑规则，只要它是非缔合性的，就可以使所得的内存状态同时表示项目信息和顺序信息。将任意数量的项目捆绑在一起而产生的记忆状态是无均匀的，并且具有一定程度的稀疏性，这是由总和中的激活阈值控制的。提出的捆绑操作允许在时间和项目的域中构建过滤器，该滤镜可用于导航信息的连续流入。
Information inflow into a computational system is by a sequence of information items. Cognitive computing, i.e. performing transformations along that sequence, requires to represent item information as well as sequential information. Among the most elementary operations is bundling, i.e. adding items, leading to 'memory states', i.e. bundles, from which information can be retrieved. If the bundling operation used is associative, e.g. ordinary vector-addition, sequential information can not be represented without imposing additional algebraic structure. A simple stochastic binary bundling rule inspired by the stochastic summation of neuronal activities allows the resulting memory state to represent both, item information as well as sequential information as long as it is non-associative. The memory state resulting from bundling together an arbitrary number of items is non-homogeneous and has a degree of sparseness, which is controlled by the activation threshold in summation. The bundling operation proposed allows to build a filter in the temporal as well as in the items' domain, which can be used to navigate the continuous inflow of information.
