The paper addresses the challenge of aligning neural recordings across different domains by focusing on low-dimensional latent dynamics. ERDiff is introduced as a solution to preserve the spatio-temporal structure during alignment, outperforming existing methods. The method is validated through experiments on synthetic and real-world datasets, showcasing its effectiveness in enhancing behavior decoding performance.
The study emphasizes the importance of understanding latent dynamics for accurate alignment in neuroscience applications. By leveraging a diffusion model, ERDiff successfully extracts and recovers the spatio-temporal structure, leading to improved alignment results. The method's ability to maintain performance under low sampling densities highlights its robustness.
Key points include:
To Another Language
from source content
arxiv.org
Key Insights Distilled From
by Yule Wang,Zi... at arxiv.org 03-12-2024
https://arxiv.org/pdf/2306.06138.pdfDeeper Inquiries