PDF(Pubmed)
Abstract：
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.
摘要：
丝足是一种薄的突触突起，早就知道在早期发育中起着重要作用。最近发现，它们在成人皮层中的含量比以前认为的要丰富，比棘（纽扣形的成熟突触）更具可塑性。受到这些发现的启发，我们介绍了一个新的突触可塑性模型，它共同描述了丝足和棘的学习。该模型假设丝状体表现出强烈的竞争性学习动态-类似于加性尖峰时间依赖性可塑性（STDP）。同时它提出,如果丝伪足经历了足够的增强，他们巩固成刺。脊柱跟随弱竞争性学习，传统上与乘法相关，STDP的软边界模型。这使得刺对输入相关性的精细结构更加稳定和敏感。我们表明，我们的学习规则具有与加性STDP相当的选择性，并且代表输入相关性以及STDP的乘法模型。我们还展示了它如何保护先前形成的记忆并充当突触巩固机制。总的来说,我们的结果可以看作是一个现象学的描述，说明丝状体和棘如何合作，以克服强弱竞争的困难。
