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)

中文翻译：

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感知网络中的吸引集

本文档给出了 [1] 中使用的模型的规范。它提供了一种使用遗传算法优化某些输入和（嘈杂）网络吸引子之间的互信息的简单方法。该网络的节点被建模为“感知的接口理论”[2] 中描述的结构的简化版本。因此，该系统被称为“感知网络”。本论文是 [1] 技术部分的编辑版本，并作为 Python 实现 PerceptualNetworks 的随附文本，可在 [3] 下免费获得。1. Prentner, R. 和 Fields, C.. 使用 AI 方法评估最小感知模型。开放哲学 2019, 2, 503-524。2. Hoffman, DD、Prakash, C. 和 Singh, M.. 感知的界面理论。心理公报和评论 2015, 22, 1480-1506。3. Prentner, R.. PerceptualNetworks。https://github.com/RobertPrentner/PerceptualNetworks。（2020 年 9 月 17 日访问）