A unified framework for information integration based on information geometry
We propose a unified theoretical framework for quantifying spatio-temporal interactions in a stochastic dynamical system based on information geometry. In the proposed framework, the degree of interactions is quantified by the divergence between the actual probability distribution of the system and a constrained probability distribution where the interactions of interest are disconnected. This framework provides novel geometric interpretations of various information theoretic measures of interactions, such as mutual information, transfer entropy, and stochastic interaction in terms of how interactions are disconnected. The framework therefore provides an intuitive understanding of the relationships between the various quantities. By extending the concept of transfer entropy, we propose a novel measure of integrated information which measures causal interactions between parts of a system. Integrated information quantifies the extent to which the whole is more than the sum of the parts and can be potentially used as a biological measure of the levels of consciousness.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
IllumiCraft: Unified Geometry and Illumination Diffusion for Controllable Video Generation
Although diffusion-based models can generate high-quality and high-resolution video sequences from textual or image inputs, they lack explicit integration of geometric cues when controlling scene lighting and visual appe…
3D geometryVideo GenerationCRePE: Curved Ray Expectation Positional Encoding for Unified-Camera-Controlled Video Generation
Camera-conditioned video generation requires positional encoding that remains reliable under changes in camera motion, lens configuration, and scene structure. However, existing attention-level camera encodings either pr…
Video GenerationAll in the (Exponential) Family: Information Geometry and Thermodynamic Variational Inference
While the Evidence Lower Bound (ELBO) has become a ubiquitous objective for variational inference, the recently proposed Thermodynamic Variational Objective (TVO) leverages thermodynamic integration to provide a tighter …
AllSchedulingVariational InferenceGeometry-guided Feature Learning and Fusion for Indoor Scene Reconstruction
In addition to color and textural information, geometry provides important cues for 3D scene reconstruction. However, current reconstruction methods only include geometry at the feature level thus not fully exploiting th…
3D geometry3D Scene ReconstructionIndoor Scene ReconstructionGraph Integration for Diffusion-Based Manifold Alignment
Data from individual observations can originate from various sources or modalities but are often intrinsically linked. Multimodal data integration can enrich information content compared to single-source data. Manifold a…
Data Integrationdomain classification