Neuromorphic Approach

Neuromorphic computing aims to build energy-efficient, high-performance systems inspired by the brain's architecture and function. Current research heavily focuses on spiking neural networks (SNNs), often implemented with event-based sensors and leveraging algorithms like Hebbian learning and variations of backpropagation adapted for spiking neurons, to address tasks in vision, robotics, speech processing, and other domains. This approach holds significant promise for applications requiring low power consumption and real-time processing, particularly in edge computing and resource-constrained environments like robotics and implantable medical devices.