Understanding the structural dynamics of biomolecules is crucial for uncovering biological functions. As molecular dynamics (MD) simulation data becomes more available, deep generative models have been developed to synthesize realistic MD trajectories. However, existing methods produce fixed-length trajectories by jointly denoising high-dimensional spatiotemporal representations, which conflicts with MD’s frame-by-frame integration process and fails to capture time-dependent conformational diversity. Inspired by MD's sequential nature, we introduce a new probabilistic autoregressive (ProAR) framework for trajectory generation. ProAR uses a dual-network system that models each frame as a multivariate Gaussian distribution and employs an anti-drifting sampling strategy to reduce cumulative errors. This approach captures conformational uncertainty and time-coupled structural changes while allowing flexible generation of trajectories of arbitrary length. Experiments on ATLAS, a large-scale protein MD dataset, demonstrate that for long trajectory generation, our model achieves a 7.5% reduction in reconstruction RMSE and an average 25.8% improvement in conformation change accuracy compared to previous state-of-the-art methods. For conformation sampling task, it performs comparably to specialized time-independent models, providing a flexible and dependable alternative to standard MD simulations.

Visual neural decoding not only aids in elucidating the neural mechanisms underlying the processing of visual information but also facilitates the advancement of brain-computer interface technologies. However, most current decoding studies focus on developing separate decoding models for individual subjects and specific tasks, an approach that escalates training costs and consumes a substantial amount of computational resources. This paper introduces a Prompt-Guided Generative Visual Language Decoding Model (PG-GVLDM), which uses prompt text that includes information about subjects and tasks to decode both primary categories and detailed textual descriptions from the visual response activities of multiple individuals. In addition to visual response activities, this study also incorporates a multi-head cross-attention module and feeds the model with whole-brain response activities to capture global semantic information in the brain. Experiments on the Natural Scenes Dataset (NSD) demonstrate that PG-GVLDM attains an average category decoding accuracy of 66.6% across four subjects, reflecting strong cross-subject generalization, and achieves text decoding scores of 0.342 (METEOR), 0.450 (Sentence-Transformer), 0.283 (ROUGE-1), and 0.262 (ROUGE-L), establishing state-of-the-art performance in text decoding. Furthermore, incorporating whole-brain response activities significantly enhances decoding performance by enabling the integration of distributed neural signals into coherent global semantic representations, underscoring its methodological importance for unified neural decoding. This research not only represents a breakthrough in visual neural decoding methodologies but also provides theoretical and technical support for the development of generalized brain-computer interfaces.

Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives. Our results indicate that the decoding accuracies for verbs and nouns with motion attributes were significantly higher for the dorsal pathway as compared to those for the ventral pathway. Comparative analyses reveal that the dorsal pathway significantly outperformed the ventral pathway in terms of decoding performance for verbs and nouns with motion attributes, with evidence showing that this superiority largely stemmed from higher-level visual cortices rather than lower-level ones. Furthermore, these two pathways appear to converge in their heightened sensitivity toward semantic content related to actions. These findings reveal unique visual neural mechanisms through which the dorsal and ventral cortical pathways segregate and converge when processing stimuli with different semantic categories.