大脑记忆巩固不是为了稳定，而是通过预测性遗忘来压缩信息以更好泛化。
Why the Brain Consolidates: Predictive Forgetting for Optimal Generalisation
- 用预测性遗忘机制选择性保留能预示未来的经验信息，降低记忆复杂度。
- 在高保真编码下，单次学习无法实现有效压缩，需离线迭代优化。
- 适用于理解神经表征演变，对脑科学与类脑模型有启发意义。
传统记忆巩固理论强调表征的稳定性，但难以解释表征漂移、语义化及离线重播的必要性。本文提出，高容量新皮层网络通过预测性遗忘——即选择性保留能预测未来结果或体验的信息——来优化表征的泛化能力。我们证明，预测性遗忘在信息论层面提升了存储表征的泛化边界。在高保真编码约束下，单次学习通常无法实现此类压缩；因此，高容量网络需通过时间分离、迭代精炼的离线过程，无需重新访问感官输入即可优化记忆痕迹。我们在自编码器式新皮层模型、生物合理预测编码电路以及基于Transformer的语言模型中验证了这一依赖容量的现象，并推导出巩固相关神经表征几何变化的定量预测。结果表明，离线巩固的计算作用超越稳定性，通过结果条件压缩，优化了保留与泛化的权衡。
原文摘要 · Abstract (English)
Standard accounts of memory consolidation emphasise the stabilisation of stored representations, but struggle to explain representational drift, semanticisation, or the necessity of offline replay. Here we propose that high-capacity neocortical networks optimise stored representations for generalisation by reducing complexity via predictive forgetting, i.e. the selective retention of experienced information that predicts future outcomes or experience. We show that predictive forgetting formally improves information-theoretic generalisation bounds on stored representations. Under high-fidelity encoding constraints, such compression is generally unattainable in a single pass; high-capacity networks therefore benefit from temporally separated, iterative refinement of stored traces without re-accessing sensory input. We demonstrate this capacity dependence with simulations in autoencoder-based neocortical models, biologically plausible predictive coding circuits, and Transformer-based language models, and derive quantitative predictions for consolidation-dependent changes in neural representational geometry. These results identify a computational role for off-line consolidation beyond stabilisation, showing that outcome-conditioned compression optimises the retention-generalisation trade-off.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。