Introducing DRAI: Dynamic Resonance AI for the Next Generation of Intelligence

At Halcyon AI Research, we believe the next generation of artificial intelligence won’t be built by pushing backpropagation further but by breaking away from it entirely.

Today, we're introducing a fundamentally new learning framework:

Dynamic Resonance AI (DRAI)

DRAI models neurons not as static units in a layered graph, but as dynamic oscillators. Each adjusts its rhythm based on local synchrony with others. When oscillations align, connections strengthen. When they drift out of sync, connections weaken.

This creates a biologically inspired, backpropagation-free, and energy-efficient architecture that can learn, adapt, and self-organize in real time without the computational strain of global error propagation.

Resonating Neurons in DRAI

Why It Matters

Most modern AI systems rely on massive weight matrices, global gradient calculations, and hardware-intensive training loops. That approach is powerful, but costly and biologically unrealistic.

DRAI addresses three foundational problems in AI:

• Energy consumption: DRAI updates connections only during synchrony events, which is ideal for analog and neuromorphic hardware.

• Biological plausibility: Learning emerges through local resonance, inspired by brain dynamics, not global backpropagation.

• Memory and adaptability: Synaptic patterns form naturally through resonance, allowing continual learning and resistance to forgetting.

Instead of “neurons that fire together wire together,” DRAI proposes: neurons that resonate together wire together.

The Core Model

At the mathematical level, DRAI is driven by a simple update rule:

∆wᵢᵫ = η ⋅ 〈cos(φᵢ(t) − φᵫ(t))〉

Where φ represents neuron phase, and η is the learning rate.

Learning occurs only when there is sustained synchrony between neuron phases. This model is based on Kuramoto-style coupled oscillators, giving DRAI its self-organizing properties.

Read the full formalism in the whitepaper or explore the live repo:

GitHub: https://github.com/HalcyonAIR/DRAI PDF: https://github.com/HalcyonAIR/DRAI/blob/main/Dynamic%20Resonance%20Artificial%20Intelligence%20(DRAI).pdf

Key Applications

DRAI opens the door to practical advances in several domains:

• Neuromorphic Computing: Suitable for analog chips using oscillators and memristors.

• Edge AI: Enables generative agents that learn on-device without retraining.

• Adaptive Robotics: Allows learning through movement and feedback in real time.

• Multimodal Integration: Can encode text, audio, and vision as separate frequency bands.

• Lifelong Learning: Forms stable memory attractors, reducing the risk of catastrophic forgetting.

Published Research

DRAI has been submitted under the title: Dynamic Resonance AI: A Phase-Synchronised Learning Paradigm Beyond Backpropagation

Manuscript: https://github.com/HalcyonAIR/DRAI/blob/main/DRAI_Journal1_WithAppendix.docx

Mathematical Summary: https://github.com/HalcyonAIR/DRAI/blob/main/DRAI%20Math.docx

Open, Practical, Ready to Use

DRAI is open-sourced under Apache 2.0. You can:

• Run your own networks

• Visualize oscillator coupling

• Simulate real-time learning without backpropagation

• Build neuromorphic hardware prototypes

GitHub: https://github.com/HalcyonAIR/DRAI Substack: https://open.substack.com/pub/halcyonairesearch/p/dynamic-resonance-ai

What’s Next

We are working on:

• A Python-based simulator using JAX and NumPy

• Edge-device oscillator simulation

• Integration with symbolic memory systems (AOSL)

We welcome collaborators in engineering, neuroscience, and system design who want to explore new learning architectures.

This is not a tweak. It is a step change.

Let’s build the next generation of AI together.