动词和论元在人脑中表征的组合性质

基于上下文先验的分布外脑动力学流匹配

Flow Matching with In-Context Priors for Out-of-Distribution Brain Dynamics

提出一种逐时间步条件扩散Transformer，通过注入组合语言和可选空间先验，实现未见认知任务下fMRI脑动力学的零样本生成，支持反事实神经科学。

从神经科学中汲取教训：如何通过整合动作、组合结构和事件记忆来实现安全、可解释和类人的人工智能

Lessons from Neuroscience for AI: How integrating Actions, Compositional Structure and Episodic Memory could enable Safe, Interpretable and Human-Like AI

通过整合动作、组合结构和事件记忆，改进基础模型以实现安全、可解释和类人的人工智能。

Basic Neural Units of the Brain: Neurons, Synapses and Action Potential

As a follow-up tutorial article of [29], in this paper, we will introduce the basic compositional units of the human brain, which will further illustrate the cell-level bio-structure of the brain. On average, the human brain contains about 100 billion neurons and many more neuroglia which serve to support and protect the neurons. Each neuron may be connected to up to 10,000 other neurons, passing signals to each other via as many as 1,000 trillion synapses. In the nervous system, a synapse is a structure that permits a neuron to pass an electrical or chemical signal to another neuron or to the…

Neurological Nature of Vision and Thought and Mechanisms of Perception Experiences

Understanding of the phenomena of vision and thought require clarification of the general mechanism of perception. So far, philosophical inquiries and scientific investigations have not been able to address clearly the mysteries surrounding them. The present work is an attempt to unravel the essences of these phenomenal based on the presumption of computational brain. Within this context, the natures of thought is clarified, and the basis of the experience of perception is established. And by drawing from the successes of the developed tactile vision substitution systems (TVSS), which render s…

发育对称性丧失：从自由能视角看脑启发的不变性学习

Developmental Symmetry-Loss: A Free-Energy Perspective on Brain-Inspired Invariance Learning

本文提出对称性丧失算法，通过环境对称性约束实现不变性学习，结合自由能理论和群论，揭示了高效表示的生成机制。

Riemannian geometry applied to detection of respiratory states from EEG signals: the basis for a brain-ventilator interface

During mechanical ventilation, patient-ventilator disharmony is frequently observed and may result in increased breathing effort, compromising the patient's comfort and recovery. This circumstance requires clinical intervention and becomes challenging when verbal communication is difficult. In this work, we propose a brain computer interface (BCI) to automatically and non-invasively detect patient-ventilator disharmony from electroencephalographic (EEG) signals: a brain-ventilator interface (BVI). Our framework exploits the cortical activation provoked by the inspiratory compensation when the …

神经符号解码神经活动

Neuro-Symbolic Decoding of Neural Activity

NEURONA通过结合符号推理与fMRI基础，提升神经活动解码的准确性和泛化能力。

Cascade and Parallel Convolutional Recurrent Neural Networks on EEG-based Intention Recognition for Brain Computer Interface

Brain-Computer Interface (BCI) is a system empowering humans to communicate with or control the outside world with exclusively brain intentions. Electroencephalography (EEG) based BCIs are promising solutions due to their convenient and portable instruments. Motor imagery EEG (MI-EEG) is a kind of most widely focused EEG signals, which reveals a subjects movement intentions without actual actions. Despite the extensive research of MI-EEG in recent years, it is still challenging to interpret EEG signals effectively due to the massive noises in EEG signals (e.g., low signal noise ratio and incom…

Exploring the Human Connectome Topology in Group Studies

Visually comparing brain networks, or connectomes, is an essential task in the field of neuroscience. Especially relevant to the field of clinical neuroscience, group studies that examine differences between populations or changes over time within a population enable neuroscientists to reason about effective diagnoses and treatments for a range of neuropsychiatric disorders. In this paper, we specifically explore how visual analytics tools can be used to facilitate various clinical neuroscience tasks, in which observation and analysis of meaningful patterns in the connectome can support patien…

基于fMRI的自然经验预测的局部性和低维性

Locality and low-dimensions in the prediction of natural experience from fMRI

功能性磁共振成像（fMRI）提供了对人脑复杂功能的动态访问，详细描述了数百个连续时间点期间数千个体素的血液动力学活动。阐明功能磁共振成像和认知功能之间联系的一种方法是通过解码;体素活动的时间序列如何结合起来提供有关内部和外部经验的信息？在这里，我们寻求功能磁共振成像解码模型之间的简单性，他们的解释和预测的有效性平衡。我们使用沉浸在虚拟现实中的受试者的信号，比较应用线性和非线性降维技术的全局和局部预测方法。我们发现，复杂刺激的预测是非常低维的，饱和小于100个功能。特别是，我们建立有效的模型的基础上，在经典定义的布罗德曼领域的认知活动的去相关组件。对于某些刺激，最高预测区域出奇地透明，包括用于言语指令的韦尼克区、用于面部和身体特征的视觉皮层以及用于速度的视觉颞叶区域。直接的感官体验导致了最强大的预测，最高的相关性（$c \sim 0.8$）之间的预测和经验丰富的时间序列的口头指令。基于非线性降维（拉普拉斯特征映射）的技术表现类似。我们的方法的可解释性和相对简单性提供了一个概念基础，在此基础上建立更复杂的功能磁共振成像解码技术，并提供了一个窗口，认知功能在动态的，自然的经验。