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Topological Vortex Theory and Its Applications in Artificial Intelligence (2)

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已发布 2025-12-17 02:30:53

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2. The Core Connotation of Topological Vortex Theory

2.1 Spacetime Dynamic Network Model

TVT constructs dynamic networks using spacetime vortices as fundamental units, simulating information flow in complex systems through the generation, evolution, and interaction of vortices. In AI, this model can capture the nonlinear dynamics of neuronal population activation and inhibition, particularly suitable for simulating "insight" phenomena in creative thinking—the sudden reorganization of information within the spacetime topological structure.

2.2 Dynamic Memory Storage Mechanism

Unlike static memory storage in traditional artificial neural networks, TVT introduces a phase decay mechanism of the vortex field to describe memory consolidation and forgetting. This mechanism more closely approximates memory regulation based on synaptic plasticity in the biological brain, endowing AI systems with more natural memory lifecycle management capabilities.

2.3 Nonlinear Dynamics and Jumping Thought

TVT emphasizes topological mutations (such as the Berezinskii–Kosterlitz–Thouless phase transition mechanism) caused by vortex interactions. Such mutations can simulate discontinuous, jumping associations in human thinking. This characteristic provides a mathematical model for AI to achieve logical leapfrogging and creative reasoning.

2.4 Multimodal Information Integration

Through the unified spacetime representation of the vortex field, TVT can map multimodal information such as vision, language, and touch into the same topological structure, reducing the computational redundancy of feature fusion in traditional multimodal AI and improving information processing efficiency and system synergy.

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