Studies have shed light on the differences and similarities in how the human brain and artificial neural networks learn and process information, revealing significant advances and pending challenges in artificial intelligence (AI).

A study from the University of Oxford has proposed a new principle to explain how the brain adjusts connections between neurons during learning. This principle, named "prospective configuration," suggests that the human brain first prepares neuronal activity in an optimal configuration before adjusting synaptic connections. This contrasts with artificial neural networks (ANNs), where an external algorithm modifies connections to reduce errors.

The "prospective configuration" could inspire faster and more robust learning algorithms for AI, leveraging the brain's ability to learn new information efficiently and with minimal interference with existing knowledge.

Another study focuses on how the human brain and computational models based on artificial neural networks assemble knowledge. Researchers found that as participants learned new information, the representation of objects in the brain "reorganized." By applying a similar process in a computational model, they managed to assemble and reassemble acquired knowledge quickly, suggesting that understanding this knowledge assembly process in humans can inform the development of computational techniques that replicate this "knowledge assembly process".

A comprehensive analysis of artificial neural networks and spike neural networks (SNNs) in the paper “Brain-inspired learning in artificial neural networks: a review” highlights current challenges and future opportunities. While ANNs have driven significant advances in AI, they face limitations such as high energy consumption and difficulties in processing dynamic and noisy data. SNNs, more closely inspired by the biology of the human brain, offer potential advantages such as improved energy efficiency and the ability to process dynamic data. However, SNNs are still in the early stages of development and require innovative solutions to optimize their synaptic weights and fully leverage their potential.

These studies underscore both the inspiration that neuroscience offers to artificial intelligence and the pending challenges in making artificial neural networks more closely mimic the learning and processing processes of the human brain.