NDAI-NeuroMAP is the first dense vector embedding model for neuroscience-specific high-precision information retrieval. It uses a massive domain-specific learning corpus of 500,000 triplets (query-positive-negative configurations), 250,000 neuroscientific definition items, and 250,000 structured knowledge graph triplets extracted from authoritative neuroscience ontologies. It uses a sophisticated fine-tuning approach that implements a multi-objective optimization framework that combines contrastive learning and triplet-based metric learning paradigms, leveraging the FremyCompany/BioLORD-2023-based model. Comprehensive evaluations on a holdout test dataset of ~24,000 neuroscience-specific queries demonstrate significant performance improvements over existing state-of-the-art general-purpose and biomedical embedding models. These experimental results highlight the importance of domain-specific embedding architectures for neuroscience-oriented RAG systems and related clinical NLP applications.
