英国牛津大学Everton J. Agnes研究组在研究中取得进展。他们的研究证明生物网络中快速、稳定和持久的记忆是兴奋和抑制可塑性共同作用的结果。相关论文发表在2024年3月20日出版的《自然-神经科学》杂志上。

为了帮助理解实验观察到的现象，研究人员设计了新的实验可将不同连接类型之间的突触共同依赖性形式化。由此产生的模型解释了抑制性如何影响兴奋可塑性，而邻近的兴奋-兴奋相互作用如何决定长期潜能的强度。

此外，研究还展示了兴奋性突触和抑制性突触之间的相互作用，是如何影响各种突触权重曲线的快速上升和长期稳定，例如协同作用突触的方向调谐和树突集群。在递归神经元网络中，共依赖可塑性产生了丰富而稳定的类似运动皮质的动态，具有很高的输入灵敏度。该研究结果表明，相邻突触相互作用在学习过程中扮演着重要角色，它将微观层面的生理学与整个神经网络现象联系在一起。

据了解，大脑的功能是通过突触的可塑性产生和维持的。当突触可塑性发生变化时，它们之间也会产生相互影响。这种"共同依赖"的性质很难通过实验来揭示，因为这需要同时监测多个突触。

附：英文原文

Title: Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks

Author: Agnes, Everton J., Vogels, Tim P.

Issue&Volume: 2024-03-20

Abstract: The brain’s functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity, they also affect each other. The nature of such ‘co-dependency’ is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalizes synaptic co-dependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity while neighboring excitatory–excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, co-dependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighborly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.

DOI: 10.1038/s41593-024-01597-4

Source: https://www.nature.com/articles/s41593-024-01597-4