Learning Multimodal Population Relations for Cross-recording Neuron Matching

Establishing reliable neuronal correspondences across recordings is essential for comparing neural dynamics across individuals and linking functional activity to circuit organization. However, this remains challenging because neuronal identities cannot be reliably determined from individual features alone, as spatial organization and functional activity patterns vary across recordings. Here, we propose NeuReL, a supervised neuron matching framework that integrates multimodal population relations by jointly learning positional and functional relationships among neurons within their surrounding populations. NeuReL first captures population-level representations within each recording and then refines correspondences by enforcing relational consistency across recordings. On held-out animals, NeuReL achieves significant improvements over the strongest evaluated baselines across multiple C. elegans datasets. We further validate NeuReL on longitudinal neuron matching in zebrafish, demonstrating its applicability across species and recording conditions. These results demonstrate that multimodal population relations provide a powerful basis for cross-recording neuron matching, enabling reliable neuronal correspondences for comparing neural activity across experimental sessions and individuals.