Reference : Feedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Chemistry
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/31876
Feedforward Chemical Neural Network: An In Silico Chemical System That Learns XOR
English
Blount, Drew []
Banda, Peter mailto [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]
Aug-2017
Artificial Life
MIT Press
23
3
295-317
Yes (verified by ORBilu)
International
1064-5462
1530-9185
[en] chemical reaction network ; cellular compartment learning ; feedforward ; error backpropagation ; linearly inseparable function
[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.
http://hdl.handle.net/10993/31876
10.1162/ARTL_a_00233
http://www.mitpressjournals.org/doi/abs/10.1162/ARTL_a_00233

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