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The evolutionary balance between innate and learned behaviors is highlyintricate, and different organisms have found different solutions to thisproblem. We hypothesize that the emergence and exact form of learning behaviorsis naturally connected with the statistics of environmental fluctuations andtasks an organism needs to solve. Here, we study how different aspects ofsimulated environments shape an evolved synaptic plasticity rule in static andmoving artificial agents. We demonstrate that environmental fluctuation anduncertainty control the reliance of artificial organisms on plasticity.Interestingly, the form of the emerging plasticity rule is additionallydetermined by the details of the task the artificial organisms are aiming tosolve. Moreover, we show that co-evolution between static connectivity andinteracting plasticity mechanisms in distinct sub-networks changes the functionand form of the emerging plasticity rules in embodied agents performing aforaging task.

Environmental variability and network structure determine the optimal plasticity mechanisms in embodied agents