LIVN: A testbed for learning to interact with in vitro neural networks

Abstract

AO_SCPLOWBSTRACTC_SCPLOWThe investigation of cultured biological neural networks is a critical frontier in neuroscience with profound implications for the advancement of brain-machine interfaces, treatments of neurological diseases, and fundamental insights into neural computation and cognition. Advances in induced pluripotent stem cell (iPSC) technology and machine learning are converging to enable novel approaches to interrogating neural circuits in vitro. However, progress in this emerging field is hampered by the technical challenges and resource-intensive nature of acquiring datasets suitable for machine learning. Experimental recordings typically do not allow for interactive learning and generally lack ground-truth information that would enable rigorous algorithm development and validation. To overcome the limitations of experimental setups, simulated biophysical models of neurons and neuronal networks can serve as crucial accelerators. They allow for more controlled and systematic exploration than currently possible with living cultures and serve as interpretable intermediaries between abstract computational theory and complex biological reality. Using this approach, we introduce livn: an open source interactive simulation environment for learning to control in vitro neural networks. livn generates synthetic neural data with ground truth at scale, enabling the development and testing of ML models in interactive settings that mimic experimental platforms. We describe benchmark tasks that challenge ML models to exploit simulated neural dynamics and release generated synthetic datasets that mimic in vitro systems. By providing an open, extensible platform for developing and benchmarking machine learning models, livn aims to accelerate progress in both ML-driven understanding and engineering of in vitro neural systems and fundamental understanding of computation in biological neural networks.