Lo-fi whole brain emulation (WBE) is an intriguing and possibly shorter-term goal for achieving mind uploading than recording all neurons from an organism’s central nervous system during natural behavior paradigms. In this talk I will give a systems and behavioral neuroscience overview on developing behavior- and neural-datasets paradigms for building High-precision Multi-modal dataset Behavior Models (HMBM) as a path for achieving lo-fi WBE for biological organisms. I will provide a background on single neuron and coarse grained neural recordings – and the significant advances in neural recordings in the past few decades. I will briefly review the neuroscience of decision making and proposal minimal neural recording criteria. I will propose a Turing test for agency and lo-fi WBE based on my previous work (see Mitelut et al 2023) as a test for achieving sufficiently accurate models of a modeled agent’s natural behavior. I will also present preliminary findings from a multi-year project at NYU that I developed for capturing multi-week behaviors from naturally behaving rodents towards building behavior models and the challenges, opportunities and paths forward for such paradigms. I will conclude with a concrete proposal for moving the field forward including improving data quality, acquisition methods, computational methods and multi-disciplinary collaborations.