Compared to physical systems that exhibit limited and mostly predetermined responses to external inputs, URLanisms show an enormous diversity of traits and an ability to evolve. Particular responses to different environmental conditions are developed URLanisms to adapt to varying environments. We study how such responses evolve by considering them as a mapping from environmental cues to phenotypic traits , modeled by a network with hidden units. An optimized mapping represents a strategy of adaptation that best URLanisms to survive and reproduce. Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies characterized by the shape of phenotype distributions. In particular, three previously studied strategies emerge as special limits of our model:  when environmental cues are URLanisms rely on the cue to express the favorable phenotype in each environmental condition ("tracking strategy"), when cues are URLanisms either express a same phenotype in all environments ("unvarying strategy ") or, remarkably, use the uninformative cue as a source of randomness to diversify into subpopulations of different phenotypes ("bet-hedging strategy"). The capacity of exploring diverse responses and evolving different strategies is associated with having a high-dimensional internal representation of the environment. Our model reveals a unifying picture of biological adaptation and provides a framework for understanding how the relations URLanisms and the environment are shaped by the evolutionary process .
URLanisms exhibit diverse strategies for adapting to varying environments. For example, a population URLanisms may express the same phenotype in all environments (`unvarying strategy'), or follow environmental cues and express alternative phenotypes to match the environment (`tracking strategy'), or diversify into coexisting phenotypes to cope with environmental uncertainty (`bet-hedging strategy'). We introduce a general framework for studying URLanisms respond to environmental variations, which models an adaptation strategy by an abstract mapping from environmental cues to phenotypic traits . Depending on the accuracy of environmental cues and the strength of natural selection, we find different adaptation strategies represented by mappings that maximize the longterm growth rate of a population. The previously studied strategies emerge as special cases of our model: the tracking strategy is favorable when environmental cues are accurate, whereas when cues are URLanisms can either use an unvarying strategy or, remarkably, use the uninformative cue as a source of randomness to bet-hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on having a high-dimensional internal representation , which can even be random .
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Compared to physical systems that exhibit limited and mostly predetermined responses to external inputs, URLanisms show an enormous diversity of traits and an ability to evolve. Particular responses to different environmental conditions are developed URLanisms to adapt to varying environments. We study how such responses evolve by considering them as a
URLanisms exhibit diverse strategies for adapting to varying environments. For example, a population URLanisms may express the same phenotype in all environments (`unvarying strategy'), or follow environmental cues and express alternative phenotypes to match the environment (`tracking strategy'), or diversify into coexisting phenotypes to cope with environmental uncertainty (`bet-hedging strategy'). We introduce a general framework for studying URLanisms respond to environmental variations, which models an adaptation strategy by an abstract
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, modeled by a network with hidden units. An optimized mapping represents a strategy of adaptation that best URLanisms to survive and reproduce.
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characterized by the shape of phenotype distributions. In particular, three
represented by mappings that maximize the longterm growth rate of a population. The
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limits
cases
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the tracking strategy is favorable
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URLanisms rely on the cue to express the favorable phenotype in each environmental condition ("tracking strategy"),
accurate, whereas
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either express a same phenotype in all environments ("unvarying strategy ")
can either use an unvarying strategy
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diversify into subpopulations of different phenotypes ("bet-hedging strategy"). The capacity of exploring diverse responses and evolving different strategies is associated with
bet-hedge. Our model of the environment-to-phenotype mapping is based on a network with hidden units; the performance of the strategies is shown to rely on
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of the environment. Our model reveals a unifying picture of biological adaptation and provides a framework for understanding how the relations URLanisms and the environment are shaped by the evolutionary process
, which can even be random