Compositional Neuro-Symbolic Concepts in Neural Activities

We propose NEURONA, a modular neuro-symbolic framework for fMRI decoding and concept grounding in neural activity. Leveraging image- and video-based fMRI question-answering datasets, NEURONA learns to decode interacting concepts from visual stimuli from patterns of fMRI signals, integrating symbolic reasoning and compositional execution with fMRI grounding across brain regions. We demonstrate that incorporating structure into the decoding pipeline improves both decoding accuracy and generalization performance. NEURONA shows that modeling the compositional structure of concepts through hierarchical predicate-argument dependencies enables more precise decoding from fMRI, highlighting neuro-symbolic frameworks as promising tools for neural decoding.