From Stochasticity to Stability: A Theoretical Framework for 'Directed In Vitro Evolution' in Organoid Intelligence

Organoid Intelligence (OI) is a rapidly emerging field that aims to revolutionize biocomputing. However, a significant limitation remains: the high variation in biological tissues. Even under identical culture conditions, organoids exhibit different learning rates and neural architectures. This inconsistency hinders their use as reliable "biological hardware." This paper proposes a novel theoretical model termed "Directed In Vitro Evolution." Instead of relying on random growth, it is proposed that microfluidic chip technologies can be used to apply a specific "selection pressure." Through closed-loop feedback systems, neural networks with desired computational traits can be theoretically selected and stabilized. This model integrates the molecular mechanisms of Calcium (Ca2+) signaling and the cAMP/PKA pathway to translate temporary electrical stimuli into permanent structural changes. Ultimately, this framework aims to transition from stochastic biological noise to standardized, programmable bioware.