Networks of Classical Conditioning Gates and Their Learning

TL;DR

This paper introduces a novel network of classical conditioning gates designed for chemical AI, proposing a learning algorithm to enable complex functions in molecular machines capable of classical conditioning.

Contribution

It develops a new model of classical conditioning gates and a learning algorithm for networks of such gates, advancing chemical AI towards complex, learnable molecular systems.

Findings

Proposed a model of classical conditioning gates.

Developed a learning algorithm for networks of these gates.

Lays groundwork for chemically synthesized learning machines.

Abstract

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.