Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and Optophysiology Simulation Testbed

Systems neuroscience has experienced an explosion of new tools for reading and writing neural activity, enabling exciting new experiments (e.g., all-optical interrogation, closed-loop control) for interrogating neural circuits. Unfortunately, these advances have drastically increased the complexity of designing experiments, with ad hoc decisions often resulting in suboptimal or even failed experiments. Bridging model and experiment via simulation can help solve this problem, leveraging advances in computational models to provide a low-cost testbed for experiment design, model validation, and methods engineering. Specifically, we require an integrated approach that incorporates simulation of the experimental interface into computational models, but no existing tool integrates optogenetics, two-photon calcium imaging, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed Cleo: the Closed-Loop, Electrophysiology, and Optophysiology experiment simulation testbed. Cleo is a Python package enabling injection of virtual recording and stimulation devices as well as closed-loop control with realistic latency into a Brian spiking neural network model. Notably, it is the only publicly available tool to date simulating two-photon and multi-opsin/wavelength optogenetics. To facilitate adoption and extension by the community, Cleo is open-source, modular, tested, and documented, and can export results to various data formats. Here we describe the design and features of Cleo, evaluate output of individual components and integrated experiments, and demonstrate its utility for advancing optogenetic techniques in prospective experiments using previously published systems neuroscience models.