# Networks of Classical Conditioning Gates and Their Learning

arXiv: https://arxiv.org/abs/2312.15161 · HTML: https://mlsanity.com/paper/2312.15161/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 23 Dec 2023.

## Builds on (most cited first, 1 of 1)

- [Data-Driven Learning of Boolean Networks and Functions by Optimal Causation Entropy Principle (BoCSE)](https://mlsanity.com/paper/2006.01023/) (2020)

## Abstract (arXiv)

Chemical AI is chemically synthesized artificial intelligence that has the ability of learning in addition to information processing. A research project on chemical AI, called the Molecular Cybernetics Project, was launched in Japan in 2021 with the goal of creating a molecular machine that can learn a type of conditioned reflex through the process called classical conditioning. If the project succeeds in developing such a molecular machine, the next step would be to configure a network of such machines to realize more complex functions. With this motivation, this paper develops a method for learning a desired function in the network of nodes each of which can implement classical conditioning. First, we present a model of classical conditioning, which is called here a classical conditioning gate. We then propose a learning algorithm for the network of classical conditioning gates.