Title: Approximated Anomalous Diffusion: Gaussian Mixture Score-based Generative Models. Abstract: Score-based generative models (SGMs) can generate high-quality samples via Langevin dynamics with a drift term and a diffusion term (Gaussian noise) iteratively calculated and added to a sample until convergence. In biological systems, it is observed that the neural population can conduct heavy-tailed L\'{e}vy dynamics for sampling-based probabilistic representation through neural fluctuations. Critically, unlike the existing sampling process of SGMs, L\'{e}vy dynamics can produce both large jumps and small roaming to explore the sampling space, resulting in better sampling results than Langevin dynamics with a lacking of large jumps. Motivated by this contrast, we explore a new class of SGMs with the sampling based on the L\'{e}vy dynamics. However, exact numerical simulation of the L\'{e}vy dynamics is significantly more challenging and intractable. We hence propose an approximation solution by leveraging Gaussian mixture noises during training to achieve the desired large jumps and small roaming properties. Theoretically, GM-SGMs conduct a probabilistic graphical model used by empirical Bayes for sampling, expanding the maximum a posteriori (MAP) estimation applied by conventional SGMs. Expensive experiments on the challenging image generation tasks show that our GM-SGMs exhibit superior sampling quality over prior art SGMs across various sampling iterations.