Biological plasticity is ubiquitous. How does the brain navigate this complex plasticity space, where any component can seemingly change, in adapting to an ever-changing environment? We build a systematic case that stable continuous learning is achieved by structured rules that enforce multiple, but not all, components to change together in specific directions. This rule-based low-dimensional plasticity manifold of permitted plasticity combinations emerges from cell type-specific molecular signaling and triggers cascading impacts that span multiple scales. These multiscale plasticity manifolds form the basis for behavioral learning and are dynamic entities that are altered by neuromodulation, metaplasticity, and pathology. We explore the strong links between heterogeneities, degeneracy, and plasticity manifolds and emphasize the need to incorporate plasticity manifolds into learning-theoretical frameworks and experimental designs.

中文翻译：

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通过结构化多尺度塑性流形实现稳定的持续学习。


生物可塑性无处不在。大脑如何驾驭这个复杂的可塑性空间，在这个空间中，任何组件似乎都可以发生变化，以适应不断变化的环境？我们构建了一个系统案例，通过结构化规则来实现稳定的持续学习，这些规则强制多个（但不是全部）组件在特定方向上一起改变。这种基于规则的低维可塑性流形允许的可塑性组合产生于细胞类型特异性分子信号传导，并触发跨越多个尺度的级联影响。这些多尺度可塑性流形构成了行为学习的基础，并且是通过神经调节、化塑性和病理学改变的动态实体。我们探索了异质性、简并性和可塑性流形之间的紧密联系，并强调将可塑性流形纳入学习理论框架和实验设计的必要性。