Chemical Boltzmann Machines

Abstract

How smart can a micron-sized bag of chemicals be? How can an artificial or real cell make inferences about its environment? From which kinds of probability distributions can chemical reaction networks sample? We begin tackling these questions by showing four ways in which a stochastic chemical reaction network can implement a Boltzmann machine, a stochastic neural network model that can generate a wide range of probability distributions and compute conditional probabilities. The resulting models, and the associated theorems, provide a road map for constructing chemical reaction networks that exploit their native stochasticity as a computational resource. Finally, to show the potential of our models, we simulate a chemical Boltzmann machine to classify and generate MNIST digits in-silico.

arXiv commentTo Appear at DNA 23 Conference

References in corpus (3)

• Universality of biochemical feedback and its application to immune cells
• Collective regulation by non-coding RNA
• Associative memory by collective regulation of non-coding RNA

Cited by in corpus (10)

• Learning without neurons in physical systems
• Conservation Laws and Work Fluctuation Relations in Chemical Reaction Networks
• Molecular and DNA Artificial Neural Networks via Fractional Coding
• Programming and Training Rate-Independent Chemical Reaction Networks
• Stochastic Chemical Reaction Networks for Robustly Approximating Arbitrary Probability Distributions
• A reaction network scheme which implements inference and learning for Hidden Markov Models
• Emergence, Construction, or Unlikely? Navigating the Space of Questions regarding Life's Origins
• Equilibrium Propagation: the Quantum and the Thermal Cases
• Rebooting Neuromorphic Hardware Design -- A Complexity Engineering Approach
• Neural CRNs: A Natural Implementation of Learning in Chemical Reaction Networks