Department of Bioengineering, Imperial College London, London, United Kingdom.
PLoS Comput Biol. 2024 May 14;20(5):e1012110. doi: 10.1371/journal.pcbi.1012110. eCollection 2024 May.
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.
丝状伪足是一种细的突触突起,长期以来一直被认为在早期发育中起着重要作用。最近,人们发现丝状伪足在成年皮质中的丰度比以前认为的要高,而且比棘突(按钮状成熟突触)更具可塑性。受这些发现的启发,我们提出了一个新的突触可塑性模型,该模型共同描述了丝状伪足和棘突的学习过程。该模型假设丝状伪足表现出强烈的竞争学习动态——类似于加性尖峰时间依赖可塑性(STDP)。同时,它提出如果丝状伪足经历足够的增强,它们就会整合为棘突。棘突遵循弱竞争学习,与经典的乘法、软约束 STDP 模型相关联。这使得棘突更加稳定,对输入相关性的细微结构也更加敏感。我们表明,我们的学习规则具有与加性 STDP 相当的选择性,并能很好地捕获输入相关性以及 STDP 的乘法模型。我们还展示了它如何保护以前形成的记忆并执行突触巩固。总的来说,我们的结果可以被看作是对丝状伪足和棘突如何合作克服强竞争和弱竞争机制所面临的个体困难的一种现象学描述。