Neuron parameters.

Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. Recently, they have been found to be more abundant in the adult cortex than previously thought, and more plastic than spines (button-shaped mature synapses). Inspired by these findings, we introduce a new model of synaptic plasticity that jointly describes learning of filopodia and spines. The model assumes that filopodia exhibit strongly competitive learning dynamics -similarly to additive spike-timing-dependent plasticity (STDP). At the same time it proposes that, if filopodia undergo sufficient potentiation, they consolidate into spines. Spines follow weakly competitive learning, classically associated with multiplicative, soft-bounded models of STDP. This makes spines more stable and sensitive to the fine structure of input correlations. We show that our learning rule has a selectivity comparable to additive STDP and captures input correlations as well as multiplicative models of STDP. We also show how it can protect previously formed memories and perform synaptic consolidation. Overall, our results can be seen as a phenomenological description of how filopodia and spines could cooperate to overcome the individual difficulties faced by strong and weak competition mechanisms.丝状伪足（Filopodia）是一类细长的突触突起，长期以来被认为在神经系统早期发育过程中发挥关键作用。近期研究发现，成年大脑皮层中的丝状伪足数量远超此前预期，且相较于棘突（spines，纽扣状成熟突触）具有更强的可塑性。受上述发现启发，我们提出了一种全新的突触可塑性模型，可联合描述丝状伪足与棘突的学习动态。该模型假设丝状伪足呈现出极强的竞争性学习动力学特性——这与加性脉冲时序依赖可塑性（STDP）的机制高度相似。与此同时，模型提出：若丝状伪足获得足够的突触增强，它们便可巩固转化为棘突。棘突则遵循弱竞争性学习机制，经典上对应乘性软约束脉冲时序依赖可塑性模型。这一特性使得棘突更为稳定，且对输入关联的精细结构更为敏感。我们证明，所提出的学习规则具备与加性STDP相当的选择性，且可如乘性STDP模型一样精准捕捉输入关联特征。此外，我们还展示了该规则如何保护已形成的记忆并完成突触巩固过程。总体而言，我们的研究结果可被视为一种现象学描述，阐释了丝状伪足与棘突如何协同作用，以克服强、弱竞争性机制各自面临的局限性。