Recent advances in bioengineering have enabled the creation of biological neural networks in vitro, significantly reducing the cost, ethical hurdles, and complexity of experimentation with genuine biological neural computation. In this position paper, we argue that this trend offers a unique and timely opportunity to put our understanding of neural computation to the test. By designing artificial neural networks that can interact and control living neural systems, it is becoming possible to validate computational models beyond simulation and gain empirical insights to help unlock more robust and energy-efficient next-generation AI systems. We provide an overview of key technologies, challenges, and principles behind this development and describe strategies and opportunities for novel machine learning research in this emerging field. We also discuss implications and fundamental questions that could be answered as this technology advances, exemplifying the longer-term impact of increasingly sophisticated in vitro neural networks.

Recent advances in neuroscience and artificial intelligence (AI) have allowed us to interact and experiment with lab-grown, small brain-like networks of living neurons. We are now at a point where experimenting with these systems is growing easier and more practical. In this position paper, we argue that now is the time to advance experimentation with and understanding of these biological neural networks. By using AI models to interact with and even control these living neurons, this could help learn how to build smarter and more energy-efficient AI. We explain the main technologies, challenges, and potential directions for using these living neurons. We also explore the significant questions that this research could help answer and the long-term implications of these advancements.