drai
DRAI is a phase-synchronized learning architecture where neurons strengthen connections through resonance, enabling local, energy-efficient adaptation without backpropagation.
Science Score: 31.0%
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Repository
DRAI is a phase-synchronized learning architecture where neurons strengthen connections through resonance, enabling local, energy-efficient adaptation without backpropagation.
Basic Info
- Host: GitHub
- Owner: HalcyonAIR
- License: apache-2.0
- Language: Jupyter Notebook
- Default Branch: main
- Size: 1.12 MB
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Dynamic Resonance AI (DRAI)
Phase-Synchronized Learning Beyond Backpropagation
DRAI is a biologically inspired, resonance-based learning framework that replaces global error propagation with local oscillatory synchronization. Instead of using backpropagation, DRAI strengthens or weakens synaptic connections based on phase alignment between dynamic neural oscillators. This enables energy-efficient, fully local, and self-organizing learning—ideal for neuromorphic computing, adaptive robotics, and real-time AI systems operating at the edge.
Key Concepts
- Neurons are modeled as phase-driven oscillators
- Synaptic weights update through natural resonance, not global gradients
- Synchrony = strength, desynchrony = decay
- Strong resistance to noise and catastrophic forgetting
- Excellent fit for analog, event-driven, and distributed architectures
Why It Matters
Backpropagation is powerful but biologically implausible and computationally expensive. DRAI offers a new approach—one inspired by real neural timing, built for emergent synchronization, and designed for decentralized cognition. It scales down efficiently and learns as it moves.
License
This whitepaper and associated materials are released under the Apache License 2.0.
© 2025 Jeffery Reid, Halcyon AI Research
Patent Pending for core resonance-based learning mechanisms
Full Whitepaper
Contact
📧 jeff@halcyon.ie
🌐 https://halcyon.ie
Owner
- Login: HalcyonAIR
- Kind: user
- Repositories: 1
- Profile: https://github.com/HalcyonAIR
Citation (CITATION.CFF)
cff-version: 1.2.0
title: "Dynamic Resonance AI (DRAI)"
authors:
- family-names: Reid
given-names: Jeffery
affiliation: Halcyon AI Research
date-released: 2025-04-06
license: Apache-2.0
doi: ""
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