标题： 表示漂移与嗅觉皮层中的学习诱导稳定化 显示英文标题

标题： Representational Drift and Learning-Induced Stabilization in the Olfactory Cortex

摘要： 大脑通过神经活动的模式对外部刺激进行编码，形成对世界的内部表征。 最近的实验表明，对于给定的刺激，神经表征会随时间发生变化。 然而，观察到的“表征漂移”（RD）的机制起源仍然不清楚。 在这里，我们提出了一种生物现实的计算模型，用于研究哺乳动物嗅觉系统中的RD，结合了两个不同时间尺度上突触权重动态的机制：以天为尺度的自发波动和以秒为尺度的尖峰时间依赖可塑性（STDP）。 我们的研究表明，虽然突触权重的自发波动会导致RD，但在重复刺激呈现过程中基于STDP的学习可以减少它。 我们的模型定量地解释了嗅觉系统中RD的最新实验，并提供了关于漂移出现及其与学习关系的机制解释，这可能有助于研究其他脑区的RD。 显示英文摘要

摘要： The brain encodes external stimuli through patterns of neural activity, forming internal representations of the world. Recent experiments show that neural representations for a given stimulus change over time. However, the mechanistic origin for the observed "representational drift" (RD) remains unclear. Here, we propose a biologically-realistic computational model of the piriform cortex to study RD in the mammalian olfactory system by combining two mechanisms for the dynamics of synaptic weights at two separate timescales: spontaneous fluctuations on a scale of days and spike-time dependent plasticity (STDP) on a scale of seconds. Our study shows that, while spontaneous fluctuations in synaptic weights induce RD, STDP-based learning during repeated stimulus presentations can reduce it. Our model quantitatively explains recent experiments on RD in the olfactory system and offers a mechanistic explanation for the emergence of drift and its relation to learning, which may be useful to study RD in other brain regions.