Article
Feedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR
Artificial Life
Sponsor
This material is based upon work supported by the National Science Foundation under grants 1318833, 1518833, and 1518861.
Document Type
Article
Publication Date
7-1-2017
Subjects
- Adaptive learning,
- Neural networks -- Simulations,
- Chemical systems,
- Network topologies
Disciplines
Abstract
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.
DOI
10.1162/ARTL_a_00233
Persistent Identifier
http://archives.pdx.edu/ds/psu/21085
Citation Information
Blount, D., Banda, P., Teuscher, C., & Stefanovic, D. (2017). Feedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR. Artificial Life. Volume 23, Issue 3, p. 295-317.
This is the publisher's final pdf. Article appears in Artificial Life (http://www.mitpressjournals.org/loi/artl) and is © 2017 by Massachusetts Institute of Technology. Article is available online at: http://dx.doi.org/10.1162/ARTL_a_00233