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

⚠️🚨 WARNING: Image Error in v1.0.0 🚨⚠️ Due to a file mix-up, the second architecture image in version 1.0.0 contains inaccuracies and does not reflect the final system layout. 🛠️ This issue has been fully resolved in version 1.0.1, which includes the correct diagram and updated content. 👉 Please refer to v1.0.1 for the accurate and up-to-date version:https://zenodo.org/records/15100056 Apologies for the confusion — and thanks for your curiosity! 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.