English(EN) On the Infinite Width and Depth Limits of Predictive Coding Networks

预测编码网络在理论上可媲美反向传播

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-25 04:00

研究人员对预测编码网络（PCNs）的无限宽度和深度极限进行了理论分析，PCNs是标准反向传播的替代方案。他们的研究结果表明，对于线性残差网络，在特定参数化下，PCNs可以实现与反向传播相同的梯度计算。当模型的宽度显著大于其深度时，会收敛到反向传播的损失函数，这表明在类脑网络架构中存在局部更新的可能性。 AI

影响为替代训练方法提供了理论基础，可能影响未来的神经网络架构。

排序理由学术论文，详细介绍了神经网络训练方法的理论极限和收敛特性。[lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.AI TIER_1 English(EN) · Francesco Innocenti, El Mehdi Achour, Rafal Bogacz · 2026-05-25 04:00

关于预测编码网络无限宽度和深度极限的探讨

arXiv:2602.07697v2 Announce Type: replace-cross Abstract: Predictive coding (PC) is a biologically plausible alternative to standard backpropagation (BP) that minimises an energy function with respect to network activities before updating weights. Recent work has improved the tra…

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关于预测编码网络无限宽度和深度极限的探讨

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