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NeuroSketch: An Effective Framework for Neural Decoding via Systematic Architectural Optimization
Gaorui Zhang, Zhizhang Yuan, Jialan Yang, Junru Chen, Li Meng, Yang Yang
https://arxiv.org/abs/2512.09524 https://arxiv.org/pdf/2512.09524 https://arxiv.org/html/2512.09524
arXiv:2512.09524v1 Announce Type: new
Abstract: Neural decoding, a critical component of Brain-Computer Interface (BCI), has recently attracted increasing research interest. Previous research has focused on leveraging signal processing and deep learning methods to enhance neural decoding performance. However, the in-depth exploration of model architectures remains underexplored, despite its proven effectiveness in other tasks such as energy forecasting and image classification. In this study, we propose NeuroSketch, an effective framework for neural decoding via systematic architecture optimization. Starting with the basic architecture study, we find that CNN-2D outperforms other architectures in neural decoding tasks and explore its effectiveness from temporal and spatial perspectives. Building on this, we optimize the architecture from macro- to micro-level, achieving improvements in performance at each step. The exploration process and model validations take over 5,000 experiments spanning three distinct modalities (visual, auditory, and speech), three types of brain signals (EEG, SEEG, and ECoG), and eight diverse decoding tasks. Experimental results indicate that NeuroSketch achieves state-of-the-art (SOTA) performance across all evaluated datasets, positioning it as a powerful tool for neural decoding. Our code and scripts are available at https://github.com/Galaxy-Dawn/NeuroSketch.
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Dynamical Analysis of the HD 169142 Planet-Forming Disk: Twelve Years of High-Contrast Polarimetry
Miles Lucas, Michael Bottom, Ruobing Dong, Myriam Benisty, Mario Flock, Maria Vincent, Jonathan Williams, Kyohoon Ahn, Thayne Currie, Vincent Deo, Olivier Guyon, Tomoyuki Kudo, Lucinda Lilley, Julien Lozi, Maxwell Millar-Blanchaer, Barnaby Norris, Sebasti\'an P\'erez, Boris Safonov, Peter Tuthill, Taichi Uyama, S\'ebastien Vievard, Manxuan Zhang

Dynamical Analysis of the HD 169142 Planet-Forming Disk: Twelve Years of High-Contrast Polarimetry
We present a dynamical analysis of the HD 169142 planet-forming disk based on high-contrast polarimetric imaging over a twelve-year observational period, offering insights into its disk evolution and planet-disk interactions. This study explores the evolution of scattered-light features and their relationship with millimeter continuum emission. Archival visible-to-near-infrared scattered-light observations from NACO, SPHERE, and GPI combined with new observations from SCExAO reveal persistent n…