iSyncTab: Learning Cross-Modal Feature Sequencing for Image-Tabular Data via Neural Synchrony

Multimodal learning of image and tabular data is a challenging problem, especially given the unstructured feature representations involved. We address this by framing feature sequencing as a Column Permutation Problem and showing how reordering columns can reduce dispersion and improve training stability for multimodal learning of image and tabular data. We propose iSyncTab, an approach that enforces these learned sequential constraints through a joint classification and order-consistency objective. We introduce Neural Synchrony-guided Paired Feature Sequencing (NS-PFS), a neuroscience-inspired NeuroAI approach that aligns image and tabular feature clusters through a Hungarian assignment of their synchrony matrix, derived from cluster energy and centroid similarity. The resulting cross-modal feature sequence provides a coherent feature alignment that preserves structural synchrony between modalities. We then introduce an Order-aware Memoryaugmented Transformer (OMT) with a custom loss function that enforces these learned feature sequence constraints. Experiments on multimodal benchmarks demonstrate that systematically derived feature sequences consistently boost predictive performance and model robustness. Our findings underscore feature sequencing as an important inductive bias for structured multimodal learning of image and tabular data. Neural synchrony-driven sequencing enhances cross-modal coherence and representation quality for image and tabular data.