返回
Universal scale-free representations in human visual cortex
DOI:10.1371/journal.pcbi.1013714.png)
摘要
En 中文
How does the human brain encode complex visual information? While previous research has characterized individual dimensions of visual representation in cortex, we still lack a comprehensive understanding of how visual information is organized across the full range of neural population activity. Here, analyzing fMRI responses to natural scenes across multiple individuals, we discover that neural representations in human visual cortex follow a remarkably consistent scale-free organization-their variance decay is consistent with a power-law distribution, detected across four orders of magnitude of latent dimensions. This scale-free structure appears consistently across multiple visual regions and across individuals, suggesting it reflects a fundamental organizing principle of visual processing. Critically, when we align neural responses across individuals using hyperalignment, we find that these representational dimensions are largely shared between people, revealing a universal high-dimensional spectrum of visual information that emerges despite individual differences in brain anatomy and visual experience. Traditional analysis approaches in cognitive neuroscience have focused primarily on a small number of high-variance dimensions, potentially missing crucial aspects of visual representation. Our results demonstrate that visual information is distributed across the full dimensionality of cortical activity in a systematic way, thus revealing a key property of neural coding in visual cortex. These findings suggest that we need to move beyond low-dimensional characterizations to fully understand how the brain represents the visual world.
Keyword:
DIMENSIONALITY
OBJECT
SPACE
期刊
P
IF:
3.6
论文数:
638
被引数:
0
机构
引用论文
Deep convolutional models improve predictions of macaque V1 responses to natural images深度卷积模型改进了猕猴V1对自然图像响应的预测
A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex
NEURON
IF15
Interpreting neural computations by examining intrinsic and embedding dimensionality of neural activity通过检查神经活动的内在和嵌入维度来解释神经计算

