https://itamar-arel.com/beyond-backpropagation-toward-a-biologically-plausible-learning-framework/

Backpropagation has long served as the backbone of modern artificial intelligence, enabling systems to learn through precise error correction across multiple layers. However, insights from neuroscience suggest that biological learning follows a very different path. The brain does not appear to calculate global gradients or rely on a single objective function. Instead, learning emerges through decentralized activity, in which smaller neural units adjust based on local signals and environmental inputs. 

This alternative view highlights a promising direction for future AI systems. Rather than relying on a single unified learning rule, models could be built from many smaller components that learn independently. These components would focus on recognizing patterns within their own scope, while broader signals—such as reward or relevance—guide which information becomes important. As a result, learning becomes more adaptive and efficient, allowing systems to prioritize meaningful experiences. This biologically inspired framework may lead to more resilient and flexible AI that better reflects how intelligence develops in natural systems. Learn More Here... 


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