# Scaling Laws for Neural Foundation Models **Entity class:** Machine-learning research concept **Domain:** Neuroscience / Foundation models **Maturity:** Developed ## Definition Scaling laws for neural foundation models study how decoding or representation performance changes with more participants, recording time, channels, model capacity, and compute. ## Mechanism and significance The research question is whether neural-data models exhibit predictable gains that could turn decoding quality into an infrastructure and dataset-scale problem. ## Relationships - **Research dossier:** [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] - **Ontology route:** [[ASI and RSI Timeline Ontology#Neural Decoding and Cognitive Systems|Neural Decoding and Cognitive Systems]] - **Primary fields:** [[Neural Decoding]] · [[Brain-Computer Interfaces]] · [[Functional Magnetic Resonance Imaging]] - **Adjacent concepts:** [[Meta Neural Decoding Research]] · [[Neuralink Electrophysiological Data]] · [[Full-Bandwidth Brain-Computer Interface]] · [[Full-Dive Virtual Reality]] ## Sources and provenance - [[research/ASI and RSI Timeline Research Moonshots|ASI and RSI Timeline Research Moonshots]] — immediate source for this node's role in the broadcast research map. ## Evidence boundary The research dossier establishes why this entity or concept belongs in the Moonshots ontology. Time-sensitive organizational, product, policy, and performance claims should be checked against the linked primary source or a current authoritative source before reuse as settled fact.