The adaptive immune system  can detect, respond to, and learn diverse pathogens from the past experience. While the clonal selection of the T helper (Th) cells is the simple and established mechanism for learning a better recognition of new pathogens, the question  still remains unexplored is how the Th cells can learn to eliminate them by activating or deactivating appropriate immune cells after recognition . In this work, we address this problem by associating the adaptive immune URLanized by the Th cells with reinforcement learning (RL). By employing recent advancements of the deep RL, we show that the Th immune network can acquire the association of antigen patterns of pathogens and the effective responses to them . Moreover, the clonal selection as well as other inter-lymphocyte interactions are derived as a learning rule of the network. We also demonstrate that the stationary clone distribution after the learning shares characteristic features with those observed experimentally. Our theoretical framework can contribute to revising and renewing our understanding of adaptive immunity as a learning system.
The adaptive immune system of vertebrates can detect, respond to, and memorize diverse pathogens from past experience. While the clonal selection of T helper (Th) cells is the simple and established mechanism to better recognize new pathogens, the question that still remains unexplored is how the Th cells can acquire better ways to bias the responses of immune cells for eliminating pathogens more efficiently by translating the recognized antigen information into regulatory signals . In this work, we address this problem by associating the adaptive immune URLanized by the Th cells with reinforcement learning (RL). By employing recent advancements of network-based RL, we show that the Th immune network can acquire the association between antigen patterns of and the effective responses to pathogens . Moreover, the clonal selection as well as other inter-cellular interactions are derived as a learning rule of the network. We also demonstrate that the stationary clone-size distribution after learning shares characteristic features with those observed experimentally. Our theoretical framework may contribute to revising and renewing our understanding of adaptive immunity as a learning system.
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of vertebrates
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learn
memorize
D
the
D
the
R
for learning a better recognition of
to better recognize
A
that
R
learn to eliminate them by activating or deactivating appropriate immune cells after recognition
acquire better ways to bias the responses of immune cells for eliminating pathogens more efficiently by translating the recognized antigen information into regulatory signals
R
the deep
network-based
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of
between
D
pathogens
R
them
pathogens
R
inter-lymphocyte
inter-cellular
R
clone distribution after the
clone-size distribution after
R
can
may