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Integrating neurons into digital systems may enable performance infeasible with silicon alone. Here, we develop DishBrain , a system that harnesses the inherent adaptive computation of neurons in a structured environment. In vitro neural networks from human or rodent origins are integrated with in silico computing via a high-density multielectrode array. Through electrophysiological stimulation and recording, cultures are embedded in a simulated game-world, mimicking the arcade game "Pong." Applying implications from the theory of active inference via the free energy principle, we find apparent learning within five minutes of real-time gameplay not observed in control conditions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Cultures display the ability to self-organize activity in a goal-directed manner in response to sparse sensory information about the consequences of their actions, which we term synthetic biological intelligence. Future applications may provide further insights into the cellular correlates of intelligence. [Display omitted] • Improvements in performance or "learning" over time following closed-loop feedback • Learning observed from both human and primary mouse cortical neurons • Systems with stimulus but no feedback show no learning • Dynamic changes observed in neural electrophysiological activity during embodiment The DishBrain system is the first real-time synthetic biological intelligence platform that demonstrates that biological neurons can adjust firing activity in a way that suggests the ability to learn to perform goal-oriented tasks when provided with simple electrophysiological sensory input and feedback while embodied in a game-world. [ABSTRACT FROM AUTHOR]