Networks of Classical Conditioning Gates and Their Learning
This work addresses the challenge of scaling up chemical AI for more advanced learning tasks, but it appears incremental as it builds on prior concepts without demonstrating new experimental results.
The paper tackles the problem of enabling networks of molecular machines to learn complex functions by developing a model of classical conditioning gates and a learning algorithm for such networks, with the result being a proposed method for achieving this in chemical AI systems.
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.