Abstract
We present ZenBrain, an open-source memory architecture for AI systems that implements seven distinct memory layers inspired by cognitive neuroscience. Unlike existing approaches that treat AI memory as simple key-value storage, dual-layer retrieval, or LLM-driven reorganization, ZenBrain models the full lifecycle of human memory: encoding, consolidation, retrieval, decay, and active forgetting. The system integrates 12 neuroscience-inspired algorithms including FSRS-based spaced repetition, Hebbian learning dynamics for knowledge graphs, neuroscience-modeled sleep consolidation simulating hippocampal replay, emotional memory modulation, and Bayesian confidence propagation. ZenBrain is deployed in production as part of ZenAI with 9,228 passing tests, and is published as composable npm packages under the @zensation scope.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bering, Alexander, "ZenBrain: A Neuroscience-Inspired 7-Layer Memory Architecture for Autonomous AI Systems", Technical Disclosure Commons, (April 01, 2026)
https://www.tdcommons.org/dpubs_series/9683