论文标题
吸引感知网络中的集合
Attracting Sets in Perceptual Networks
论文作者
论文摘要
该文档为[1]中使用的模型提供了规范。它提出了一种使用遗传算法在（嘈杂）网络的某些输入和吸引者之间优化互信息的简单方法。该网络的节点被建模为“感知界面理论”中描述的结构的简化版本[2]。因此，该系统被称为“感知网络”。 本文是[1]的技术部分的编辑版本，并作为Python实现的随附文本CecceptualNetworks，在[3]下免费获得。 1。Prentner，R。和Fields，C.。使用AI方法评估最小模型以进行感知。 Openphilosophy 2019，2，503-524。 2。Hoffman，D。D.，Prakash，C。和M. Singh。感知界面理论。心理公告和评论2015，22，1480-1506。 3。 https://github.com/robertprentner/perceptualnetworks。 （2020年9月17日访问）
This document gives a specification for the model used in [1]. It presents a simple way of optimizing mutual information between some input and the attractors of a (noisy) network, using a genetic algorithm. The nodes of this network are modeled as simplified versions of the structures described in the "interface theory of perception" [2]. Accordingly, the system is referred to as a "perceptual network". The present paper is an edited version of technical parts of [1] and serves as accompanying text for the Python implementation PerceptualNetworks, freely available under [3]. 1. Prentner, R., and Fields, C.. Using AI methods to Evaluate a Minimal Model for Perception. OpenPhilosophy 2019, 2, 503-524. 2. Hoffman, D. D., Prakash, C., and Singh, M.. The Interface Theory of Perception. Psychonomic Bulletin and Review 2015, 22, 1480-1506. 3. Prentner, R.. PerceptualNetworks. https://github.com/RobertPrentner/PerceptualNetworks. (accessed September 17 2020)
