This chapter examines the ongoing paradigm shift from traditional animal experimentation to artificial intelligence-based approaches in neurological research. This chapter documents how over 217 research groups now employ AI-based methodologies across expanding domains. Success varies depending on the specific neurological field. Areas like neurophysiology have achieved remarkable fidelity, with AI systems producing neural simulations indistinguishable from biological activity to expert observers. However, other domains such as developmental neurobiology remain significantly limited, capturing only approximately 37% of key developmental processes. The ethical landscape has undergone a fundamental transformation, shifting from a “necessity presumption” that accepted animal models despite their ethical costs to a “replacement imperative” requiring justification for continued animal use, prompting 67% of research institutions to update their ethical guidelines since 2020. Despite these advances, substantial technical limitations persist, particularly in modeling multiscale neural complexity and detecting truly novel phenomena, as current AI systems excel at interpolation within training data bounds but struggle with extrapolation to unprecedented scenarios—a critical limitation for discovery-oriented research. Regulatory frameworks remain largely built around animal experimentation assumptions, although acceptance varies globally, with European agencies adopting more progressive stances than their counterparts elsewhere. This chapter concludes that while complete transformation remains incomplete, emerging technologies such as neuromorphic computing architectures and autonomous experimental design systems point toward an increasingly AI-dominant future for neurological research, contingent upon continued technical innovation, evolved regulatory frameworks, and sustained engagement with fundamental epistemological questions about the nature of scientific knowledge itself.