chemical reaction network; cellular compartment learning; feedforward; error backpropagation; linearly inseparable function
Résumé :
[en] 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.
Disciplines :
Chimie Sciences informatiques
Auteur, co-auteur :
Blount, Drew
BANDA, Peter ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Teuscher, Christof; Portland State University > Department of Electrical and Computer Engineering
Stefanovic, Darko; University of New Mexico > Department of Computer Science and Center for Biomedical Engineering
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Feedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR
