TMemNet-I: An Irreversible Memory Model Inspired by Biological Principles

This preprint introduces TMemNet-I, a biologically inspired neural memory system grounded in irreversible dynamics and structured temporal evolution. It builds on previous work involving KL divergence, entropy decay, and Hamiltonian memory updates to explore emergent behavior, chaos, and irreversibility in learning systems. The model bridges theoretical neuroscience, physics, and machine learning. 📌 Note: This paper was previously submitted to the Journal of Artificial Intelligence Research (JAIR) and is now shared as a preprint for visibility and community feedback. Some sections remain under development, and minor technical errors may still be present. Further refinements are ongoing. © 2025 Nathaniel MacDonald. Licensed under CC BY-NC-ND 4.0 International.This work may be shared with attribution but may not be reused, modified, or republished without explicit permission. Innovators lead. I evolve. This is only the beginning.