Hebbian learning and reinforcement learning are two distinct approaches used in learning systems, particularly in the context of neural processes and brain-machine interfaces. Hebbian learning is based on the principle that when two neurons are activated simultaneously, the connection between them becomes stronger. This local learning mechanism mimics how our brains learn from experiences. Its focus on the relationships between neurons makes Hebbian learning efficient, especially in applications like brain-machine interfaces, allowing systems to adapt and learn in real-time based on sensory input. Additionally, implementing Hebbian learning is often simpler than using more complex algorithms, leading to quicker results and easier integration into hardware, such as those being developed by Neuralink.

The Hebbian learning model offers several advantage