Recent advances in neurotechnology enable exciting new experiments, including novel paradigms such as closed-loop optogenetic control that achieve powerful causal interventions. At the same time, improved computational models are capable of reproducing behavior and neural activity with increasing fidelity. However, the complexities of these advances can make it difficult to reap the benefits of bridging model and experiment, such as in-silico experiment prototyping or direct comparison of model output to experimental data. We can bridge this gap more effectively by incorporating the simulation of the experimental interface into our models, but no existing tool integrates optogenetics, electrode recording, and flexible closed-loop processing with neural population simulations. To address this need, we have developed the Closed-Loop, Electrophysiology, and Optogenetics experiment simulation testbed (Cleo). Cleo is a Python package built on the Brian 2 simulator enabling injection of recording and stimulation devices as well as closed-loop control with realistic latency into a spiking neural network model. Here we describe the design, features, and use of Cleo, including validation of the individual system components. We further demonstrate its utility in three case studies using a variety of existing models and discuss potential applications for advancing causal neuroscience.