Yilmaz Ozgur
Turgut Ozal University, Department of Computer Engineering, Ankara 06010, Turkey ozguryilmazresearch.net.
Neural Comput. 2015 Dec;27(12):2661-92. doi: 10.1162/NECO_a_00787. Epub 2015 Oct 23.
This letter introduces a novel framework of reservoir computing that is capable of both connectionist machine intelligence and symbolic computation. A cellular automaton is used as the reservoir of dynamical systems. Input is randomly projected onto the initial conditions of automaton cells, and nonlinear computation is performed on the input via application of a rule in the automaton for a period of time. The evolution of the automaton creates a space-time volume of the automaton state space, and it is used as the reservoir. The proposed framework is shown to be capable of long-term memory, and it requires orders of magnitude less computation compared to echo state networks. As the focus of the letter, we suggest that binary reservoir feature vectors can be combined using Boolean operations as in hyperdimensional computing, paving a direct way for concept building and symbolic processing. To demonstrate the capability of the proposed system, we make analogies directly on image data by asking, What is the automobile of air?
这封信介绍了一种新型的储层计算框架,它既具备连接主义机器智能又能进行符号计算。细胞自动机被用作动态系统的储层。输入被随机投影到自动机单元格的初始条件上,并通过在自动机中应用一条规则对输入进行一段时间的非线性计算。自动机的演化创建了自动机状态空间的时空体积,并将其用作储层。所提出的框架被证明具有长期记忆能力,并且与回声状态网络相比,所需的计算量要少几个数量级。作为这封信的重点,我们建议可以像在超维计算中那样,使用布尔运算来组合二进制储层特征向量,为概念构建和符号处理铺平了一条直接的道路。为了证明所提出系统的能力,我们通过询问“空气的汽车是什么?”直接对图像数据进行类比。