Brain Learning Outshines Artificial Intelligence!

Researchers at MRC Brain Network Dynamics Unit & Oxford’s Computer Science Department introduce a groundbreaking principle, shedding light on brain network learning. This insight sparks the potential for turbocharged AI algorithms.

The essence of learning is identifying errors in the information flow. AI accomplishes this by adjusting parameters through backpropagation to minimize mistakes. However, the human brain surpasses AI in learning as it can easily retain existing knowledge after a single exposure. This has motivated researchers to explore the fundamental learning principle of the brain.

After analyzing mathematical equations describing the behavior of neurons and synaptic changes, researchers discovered a unique principle distinguishing the brain’s neural network from artificial neural networks. Unlike AI, where an external algorithm adjusts connections, the brain first optimizes neuron activity, a process called ‘prospective configuration.’ This approach preserves knowledge and accelerates the learning process.

The proposed ‘prospective configuration’ minimizes interference during learning. It explains neural behavior better than AI. Lead researcher Prof. Rafal Bogacz aims to bridge the gap between models and real brains.

Researchers have made a breakthrough in understanding the brain’s « prospective configuration », which could lead to major advances in artificial intelligence. This principle involves preserving knowledge and speeding up the learning process, ultimately bringing human cognition and AI closer together. The future looks bright for neuroscience and technology, with exciting advancements on the horizon.

Source: University of Oxford, « Study shows that the way the brain learns is different from the way that artificial intelligence systems learn »