MIT 报道 PNAS 论文：随机连接的神经元单元可自组织出学习回路
How artificial neural networks build their own learning machinery [R]
MIT 报道与 PNAS 论文《How Biological Synapses Self-Assemble Gradient Learning》提出，由共享可塑性规则支配的随机互连类神经元单元能够自行组装出学习基序，完成模式识别与图像分类，报告称其表现与所测试的反向传播训练网络相近。
MIT coverage (13 October 2026): https://news.mit.edu/2026/how-artificial-neural-networks-build-their-own-learning-machinery-1013
Paper: “How Biological Synapses Self-Assemble Gradient Learning” (PNAS, 9 October): https://www.pnas.org/doi/10.1073/pnas.2606245123
According to the MIT report, randomly interconnected neuron-like units governed by shared plasticity rules can assemble learning motifs and learn pattern recognition and image classification. The reported comparison finds performance similar to the backpropagation-trained networks tested.
The distinction is between designing a learning circuit directly and specifying local rules from which its learning machinery can organise itself. The stability of the motifs under different initial connectivity and extension beyond the demonstrated tasks are useful questions for evaluating the result alongside classification accuracy.
The authors explicitly caution that self-organisation does not establish that the brain uses this mechanism. The motivation is biological plausibility, rather than evidence of a replacement for backpropagation in large-scale models.
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来源：r/MachineLearning · reddit.com