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The Neural Structural Reasoner (NSR) is a novel brain-inspired architecture that preserves relational topology through coupled neuronal populations, addressing limitations of flat embeddings in current structural reasoning methods. By mimicking biological mechanisms like multi-layered encoding, stable representations, and path integration, NSR performs parallelized, confidence-weighted link prediction over candidate relational structures. While achieving competitive accuracy and lower training times on knowledge-graph benchmarks, NSR offers native interpretability through human-readable neuron activations and extracts latent relational hierarchies.
Brain-inspired neural networks can directly encode and reason over relational structure, unlocking interpretable inference and latent rule discovery without collapsing topology into flat embeddings.
Structural reasoning, the ability to recognize and make inferences over the relational structure between objects and concepts, is a hallmark of human cognition, yet prevailing methods often collapse relational topology into flat embeddings, cannot discover hidden structure and lack interpretability. We introduce Neural Structural Reasoner (NSR), a brain-inspired network that preserves relational structure directly in the connectivity and dynamics of coupled neuronal populations. NSR draws inspiration from three biological mechanisms: multi-layered architecture for encoding hierarchical knowledge, stable representations of entity and concepts, and path integration for input-driven state inference. At query time, NSR parallelizes computation over candidate relational structures and leverages confidence-weighted scores to perform link prediction. Across standard knowledge-graph benchmarks, NSR achieves competitive accuracy without leading on every dataset, and has lower reported training times than several neural baselines. Because reasoning is implemented through sequences of human-readable neuron activations, NSR affords native interpretability by tracking intermediate inference steps. The model further extracts latent relational hierarchies and compositional rules, demonstrating the brain-inspired architecture as an effective, efficient, and highly interpretable substrate for structural reasoning.