  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 network organized 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.
