AI Reports · MIT · 2026-10-13 · Blog / commentary
How artificial neural networks build their own learning machinery
Scientists at MIT's McGovern Institute and K. Lisa Yang ICoN Center show in a PNAS paper that an artificial neural network can build its own learning machinery. Given simple rules for how and when connections should change, randomly connected units reorganize into an effective information-processing network trained on data. The authors argue that self-organization without a developer's guidance is a key measure of biological plausibility, but caution that such networks may offer limited insight into biological learning.