NeuroBridge: Few-Shot Cross-Modal Neuron Re-identification via Dual-Channel Deep Metric Learning

Associating the in-vivo function of neurons with their ex-vivo anatomical structure is a central challenge in neuroscience. However, this field is constrained by a critical bottleneck: the extreme difficulty of acquiring paired cross-modal data, leading to a persistent scarcity of large-scale datasets. This inherent limitation frames the re-identification of the same neuron as a formidable few-shot, fine-grained visual recognition task. To address this challenge, we propose a novel deep metric learning framework designed to learn modality-invariant feature representations for single neurons under these data-scarce conditions. The core of this framework is a dual-channel network architecture that explicitly disentangles and fuses the local morphological information of the neuron's soma with the global topological context of the dendritic arbor, thereby capturing a more robust neural signature. To maximize data efficiency, we integrate a Circle Loss objective with a Multi-Similarity hard-sample mining strategy, which effectively optimizes the embedding space for better class separation. On a cross-modal neuron dataset that realistically reflects experimental data scarcity, our method demonstrates excellent performance, achieving a Recall of 77.4% and a Specificity of 90.1% on the test set. Extensive ablation studies and comparative analyses validate the effectiveness of our proposed method, establishing a new strong baseline for this critical yet data-limited biomedical application. To foster future research in this field, we will release our code, dataset, and pre-trained models.