How does the brain learn? Does it acquire new knowledge by creating new neural pathways, or by strengthening the connections that already exist? A new study from Bar-Ilan University offers evidence in favor of the latter, suggesting that learning is driven primarily by changes in the strength of existing neural connections rather than by expanding the brain's underlying architecture.
Published in Physica A , the study by Prof. Ido Kanter of Bar-Ilan University's Department of Physics and the Gonda (Goldschmied) Multidisciplinary Brain Research Center explored this longstanding question using artificial neural networks trained on language-learning tasks.
As the amount of training data increased, the models became significantly better at learning. Surprisingly, however, the researchers found that the networks could still lose roughly the same proportion of connections (synapses) without any meaningful decline in performance. In other words, improved learning did not depend on building more complex networks. Instead, it resulted from more effective cooperation among the components that were already there.
The findings suggest that learning is achieved primarily by adjusting the strength of existing connections—known as synaptic weights—rather than by continually reorganizing or expanding the network itself.
"This finding is particularly intriguing in the context of biological brains, where the number of neurons remains approximately constant," said Yanir Harel, an M.Sc. student at Bar-Ilan University and the study's first author. "Moreover, there is currently no evidence for a biological mechanism that would enable large-scale, continuous reconfiguration of neural network topology during learning."
The research points to a possible common principle shared by biological and artificial intelligence: intelligence may emerge less from adding new components and more from improving how existing ones work together. If so, both the human brain and modern AI systems may owe much of their remarkable learning ability to the continual refinement of internal connections rather than to ever-expanding architectures.
A video on two paradigms for brain learning: https://www.youtube.com/watch?v=0YmEgywvONQ
Physica A Statistical Mechanics and its Applications
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