MIT self-assembling neural networks learned pattern recognition and image classification without starting from a carefully engineered learning circuit. Instead, randomly connected units formed useful learning machinery under shared local plasticity rules.
That result changes the question researchers can ask about biologically plausible learning. A model no longer needs only to explain how an established circuit adjusts its connections. It can also explore how the circuit responsible for those adjustments emerges.
The central conflict is not simply MIT self-assembling neural networks versus conventional artificial intelligence. It is self-organized learning machinery versus backpropagation, an algorithm whose required coordination is difficult to map directly onto biology.
The researchers report performance similar to the backpropagation-trained networks they tested. However, they do not claim that their system replaces backpropagation in large AI models. They also do not claim that biological brains use the proposed mechanism.
The more important result is narrower. A collection of components can begin with random internal connections, follow local rules, and assemble motifs that approximate gradient-based learning. That shifts some design work from the network architect into the interactions among the network’s units.
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