This workshop presentation focuses on the topic of emulating increasingly complex systems. The main objective is to start with a known neural system and gradually build up using neurological models, such as neural arrays, in order to understand and replicate unknown aspects of more mysterious systems. The process involves developing metrics and criteria for success, comparing methods, and examining the plasticity of the system. The approach is scalable, starting with simpler systems and progressing to more complex ones, with the use of an iterative feedback loop to test and improve the systems. The project aims to prove that uploading systems is possible, establish success criteria and metrics, and demonstrate successful system emulation. In vivo testing, including slicing, scanning, and analyzing the emulated system, is an important aspect of the project. The ultimate goal is to contribute to AI safety and the Whole Brain Emulation (WBE) roadmap. The cost of the project depends on the desired setup and collaboration, with funding availability being a significant factor. The project is inherently iterative and scalable, with each step generating metrics for the next step. Collaboration and mentorship are crucial for progress, and ethical considerations and potential risks are taken into account during the development of technology.