![]() ![]() Indeed, some proponents suggest that generalised SBI may arrive before artificial general intelligence (AGI) due to the inherent efficiency and evolutionary advantage of biological systems. This would include, but not limited to, pseudo-cognitive responses as part of drug screening, bridging the divide between single cell and population coding approaches to understanding neurobiology, better understanding how BNNs compute to inform machine learning approaches, and potentially give rise to silico-biological computational platforms that surpass the performance of existing silicon-alone hardware. Being able to successfully interact with SBIs would enable investigations into previously untestable areas. Here we aim to establish functional in vitro networks of cortical cells from embryonic rodent and human induced pluripotent stem cells (hiPSCs) on high-density multielectrode arrays (HD-MEA) to demonstrate that these neural cultures can exhibit biological intelligence-as evidenced by learning in a simulated gameplay environment-in real time. This raises significant challenges to any attempts to generate in silico neuronal models to predict function of BNN systems. Yet, no system outside biological neurons are capable of supporting at least third-order complexity which is necessary to recreate the complexity of a biological neuronal network (BNN). The superiority of biological computation has been widely recognised with attempts to develop hardware supporting neuromorphic computing. ![]() Harnessing the computational power of living neurons to create synthetic biological intelligence (SBI), previously confined to the realm of science fiction, is now tantalisingly within the reach of human innovation. Cultures display the ability to self-organise in a goal-directed manner in response to sparse sensory information about the consequences of their actions. Further experiments demonstrate the importance of closed-loop structured feedback in eliciting learning over time. Applying a previously untestable theory of active inference via the Free Energy Principle, we found that learning was apparent within five minutes of real-time gameplay, not observed in control conditions. Through electrophysiological stimulation and recording, cultures were embedded in a simulated game-world, mimicking the arcade game ‘Pong’. In vitro neural networks from human or rodent origins, are integrated with in silico computing via high-density multielectrode array. We developed DishBrain, a system which exhibits natural intelligence by harnessing the inherent adaptive computation of neurons in a structured environment. Integrating neurons into digital systems to leverage their innate intelligence may enable performance infeasible with silicon alone, along with providing insight into the cellular origin of intelligence. ![]()
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