桥接神经活动的信息和动力学属性
Bridging the information and dynamics attributes of neural activities
摘要：
大脑是一个处理信息的动态系统。在理解大脑中信息和动力学属性之间的联系方面仍然存在各种挑战。本研究致力于探索神经信息功能的特征如何与神经动力学联系起来。我们试图弥合动态（例如，Kolmogorov-Sinai熵）和信息（例如，互信息和Fisher信息）度量的刺激触发的随机动力学在神经元群体。一方面，我们的统一分析确定了信息处理相关的神经动力学的各种基本特征。在神经信息处理过程中，我们发现神经动力学的动态随机性和混沌度存在时空差异。另一方面，我们的框架揭示了神经动力学在塑造神经信息处理中的基本作用。神经动力学在特定条件下产生编码和解码特性的反向变化，并决定刺激分布的神经表征。总的来说，我们的研究结果展示了一个潜在的方向来解释神经信息处理从神经动力学的出现，并帮助理解信息和物理大脑之间的内在联系。
英文摘要：
The brain works as a dynamic system to process information. Various challenges remain in understanding the connection between information and dynamics attributes in the brain. The present research pursues exploring how the characteristics of neural information functions are linked to neural dynamics. We attempt to bridge dynamics (e.g., Kolmogorov-Sinai entropy) and information (e.g., mutual information and Fisher information) metrics on the stimulus-triggered stochastic dynamics in neural populations. On the one hand, our unified analysis identifies various essential features of the information-processing-related neural dynamics. We discover spatiotemporal differences in the dynamic randomness and chaotic degrees of neural dynamics during neural information processing. On the other hand, our framework reveals the fundamental role of neural dynamics in shaping neural information processing. The neural dynamics creates an oppositely directed variation of encoding and decoding properties under specific conditions, and it determines the neural representation of stimulus distribution. Overall, our findings demonstrate a potential direction to explain the emergence of neural information processing from neural dynamics and help understand the intrinsic connections between the informational and the physical brain.
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