| First Author | Zang B | Year | 2025 |
| Journal | Commun Biol | Volume | 8 |
| Issue | 1 | Pages | 298 |
| PubMed ID | 39994447 | Mgi Jnum | J:389831 |
| Mgi Id | MGI:7866263 | Doi | 10.1038/s42003-025-07711-x |
| Citation | Zang B, et al. (2025) Tensor-powered insights into neural dynamics. Commun Biol 8(1):298 |
| abstractText | The complex spatiotemporal dynamics of neurons encompass a wealth of information relevant to perception and decision-making, making the decoding of neural activity a central focus in neuroscience research. Traditional machine learning or deep learning-based neural information modeling approaches have achieved significant results in decoding. Nevertheless, such methodologies require the vectorization of data, a process that disrupts the intrinsic relationships inherent in high-dimensional spaces, consequently impeding their capability to effectively process information in high-order tensor domains. In this paper, we introduce a novel decoding approach, namely the Least Squares Sport Tensor Machine (LS-STM), which is based on tensor space and represents a tensorized improvement over traditional vector learning frameworks. In extensive evaluations using human and mouse data, our results demonstrate that LS-STM exhibits superior performance in neural signal decoding tasks compared to traditional vectorization-based decoding methods. Furthermore, LS-STM demonstrates better performance in decoding neural signals with limited samples and the tensor weights of the LS-STM decoder enable the retrospective identification of key neurons during the neural encoding process. This study introduces a novel tensor computing approach and perspective for decoding high-dimensional neural information in the field. |
