Hierarchical Shallow Predictive Matter Networks (HSPMN): Integrating Multi-Scale Prediction with Parallel Processing Through Active Matter Dynamics

Hierarchical Shallow Predictive Matter Networks (HSPMN): Integrating Multi-Scale Prediction with Parallel Processing Through Active Matter DynamicsThis work introduces HSPMN, a novel AI architecture that combines hierarchical predictive coding with parallel shallow processing, inspired by neuroscience and active matter physics. The model enables dynamic resource allocation, robust continual learning, and efficient multi-scale computation. The paper includes theoretical foundations, PyTorch implementation strategies, and proposed experiments for validation.