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MIT报道PNAS新研究：随机连接的神经元单元通过共享可塑性规则可自我组装出学习结构，在模式识别和图像分类上达到与反向传播训练网络相近的性能，作者强调这是生物合理性探索而非反向传播的替代方案。
This paper proposes a framework for neocortical learning that meets criteria for computational, algorithmic, and implementational plausibility, using error-driven predictive learning via temporal derivatives and corticothalamic circuits. It suggests potential improvements over backpropagation.