Miruvor AI is a deep‑tech research lab creating a spike‑native continual learning architecture that uses spiking neurons and local learning rules to enable persistent memory and real‑time adaptation. Their models compute only when features are active and update synaptic weights via spike‑timing‑dependent plasticity, avoiding global backpropagation and catastrophic interference. The long‑term deployment target is neuromorphic silicon, allowing efficient, biologically inspired AI systems.
Funding
Funding not disclosed
Founders
Product
Problem
Current deep learning models rely on static pre‑training and lack persistent memory, so they cannot incorporate new experiences after deployment and suffer catastrophic forgetting when updated. This limits their ability to adapt in real time and makes continual learning impractical for many edge and autonomous applications.
Solution
Miruvor AI is creating a new AI architecture built around spiking neural networks that fire only when relevant features are present, enabling event‑driven computation. The system uses spike‑timing‑dependent plasticity combined with hybrid local learning rules to adjust synaptic weights directly from experience, eliminating the need for global back‑propagation. By updating weights locally, the model retains previously learned knowledge while integrating new information, achieving true continual learning without catastrophic interference. The architecture is designed for deployment on neuromorphic silicon, providing high computational efficiency and low power consumption for real‑time adaptation. This approach offers a brain‑inspired pathway to AI systems that can learn continuously, maintain persistent state, and operate efficiently on specialized hardware.
Target Audience
Primary customers are AI research labs, robotics developers, and edge‑computing firms that require continual learning capabilities and low‑power deployment on neuromorphic hardware.
Features
- Spiking neural network substrate that processes information only when neurons fire, reducing unnecessary computation
- Spike‑timing‑dependent plasticity (STDP) for biologically inspired, local weight updates based on temporal activity patterns
- Hybrid local learning rules that combine STDP with additional mechanisms to improve stability and learning speed
- Event‑driven processing that activates computation solely for active features, enhancing energy efficiency
- Architecture designed for direct mapping onto neuromorphic silicon, enabling low‑power, high‑throughput deployment
- Built‑in mechanisms to prevent catastrophic forgetting, preserving prior knowledge while learning new tasks