Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models

• URL: http://arxiv.org/abs/2306.06138v2
• Date: Fri, 8 Mar 2024 20:11:55 GMT
• Title: Extraction and Recovery of Spatio-Temporal Structure in Latent Dynamics Alignment with Diffusion Models
• Authors: Yule Wang, Zijing Wu, Chengrui Li, Anqi Wu
• Abstract summary: In behavior-related brain computation, it is necessary to align neural signals against drastic domains among them. We propose an alignment method ERDiff, which leverages the expressivity of the diffusion model to preserve the intrinsic-temporal structure of latent dynamics. Our method consistently manifests its capability of preserving thetemporal structure of latent dynamics and outperforms existing approaches in alignment goodness-of-fit and neural decoding performance.
• Score: 1.4756031289693907
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In the field of behavior-related brain computation, it is necessary to align raw neural signals against the drastic domain shift among them. A foundational framework within neuroscience research posits that trial-based neural population activities rely on low-dimensional latent dynamics, thus focusing on the latter greatly facilitates the alignment procedure. Despite this field's progress, existing methods ignore the intrinsic spatio-temporal structure during the alignment phase. Hence, their solutions usually lead to poor quality in latent dynamics structures and overall performance. To tackle this problem, we propose an alignment method ERDiff, which leverages the expressivity of the diffusion model to preserve the spatio-temporal structure of latent dynamics. Specifically, the latent dynamics structures of the source domain are first extracted by a diffusion model. Then, under the guidance of this diffusion model, such structures are well-recovered through a maximum likelihood alignment procedure in the target domain. We first demonstrate the effectiveness of our proposed method on a synthetic dataset. Then, when applied to neural recordings from the non-human primate motor cortex, under both cross-day and inter-subject settings, our method consistently manifests its capability of preserving the spatiotemporal structure of latent dynamics and outperforms existing approaches in alignment goodness-of-fit and neural decoding performance.

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