Neural Geometry Contrastive Learning: Towards Brain-Machine Intelligence Enhancement

Modern artificial neural networks (ANNs) have exhibited remarkable capabilities in learning representations from large-scale data, yet improving their complex cognitive abilities (e.g., understanding abstract concepts) remains a major challenge. One potential reason behind this limitation is that there are still many discrepancies between the representational structure formed by ANNs and that of the human brain. Therefore, we propose neural geometry contrastive learning (NeuroGeo), a novel method that learns representations from human brain activity and transfers their representational geometric structures to ANNs. Unlike most existing methods, NeuroGeo explicitly aligns geometric structures by preserving how representations vary relative to one another, thereby avoiding strict alignment constraints across heterogeneous modality spaces. This formulation yields rich combinatorial supervision from limited brain activity. Moreover, by introducing learnable virtual anchors and representing each embedding as an anchor-relative displacement, NeuroGeo establishes adaptive reference origins for different modalities and subjects, unifying cross-subject representation learning and relational structure alignment within a contrastive learning framework. We find that NeuroGeo effectively transfers human representational structures to ANNs, significantly enhancing their ability to represent abstract concepts. Results highlight that NeuroGeo yields highly interpretable concept representations, thus leading to substantial performance gains in downstream tasks such as few-shot learning and neural semantic decoding. Code will be released upon acceptance.