Feedforward Chemical Neural Network: An In Silico Chemical System That Learns xor

Drew Blount, Reed College
Peter Banda, University of Luxembourg
Christof Teuscher, Portland State University
Darko Stefanovic, University of New Mexico


Inspired by natural biochemicals that perform complex information processing within living cells, we design and simulate a chemically implemented feedforward neural network, which learns by a novel chemical-reaction-based analogue of backpropagation. Our network is implemented in a simulated chemical system, where individual neurons are separated from each other by semipermeable cell-like membranes. Our compartmentalized, modular design allows a variety of network topologies to be constructed from the same building blocks. This brings us towards general-purpose, adaptive learning in chemico: wet machine learning in an embodied dynamical system.