期刊
ARTIFICIAL LIFE
卷 23, 期 3, 页码 295-317出版社
MIT PRESS
DOI: 10.1162/ARTL_a_00233
关键词
Chemical reaction network; cellular compartment learning; feedforward; error backpropagation; linearly inseparable function
资金
- National Science Foundation [1318833, 1518833, 1518861]
- Direct For Computer & Info Scie & Enginr
- Division of Computing and Communication Foundations [1518833, 1318833, 1518861] Funding Source: National Science Foundation
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.
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