  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)
