Institute for Physics - Biophysics, Georg-August-University Göttingen, Germany.
Front Comput Neurosci. 2011 Nov 10;5:47. doi: 10.3389/fncom.2011.00047. eCollection 2011.
Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.
突触缩放是一个缓慢的过程，它可以调节突触，使神经回路的发射率保持在特定的范围内。与其他过程（如长时程抑制和长时程增强等传统的突触可塑性形式）一起，突触缩放改变了网络中的突触模式，确保了多样化、功能相关、稳定和输入依赖的连接。然而，突触模式是如何产生和稳定的，在很大程度上还不清楚。在这里，我们根据实验研究的结果，对突触缩放进行了正式的描述和分析，并证明了不同传统可塑性机制与突触缩放的结合，为调节网络连接提供了一个强大的通用框架。此外，我们设计了几个简单的模型，这些模型可以再现实验观察到的突触分布，以及在持续活动变化期间观察到的突触修饰。这些模型预测，可塑性与缩放的结合会产生全局稳定、输入控制的突触模式，即使在递归网络中也是如此。因此，与其他形式的可塑性结合，突触缩放可以稳健地产生具有高突触多样性的神经元回路，这可能为复杂激活模式的稳健动态存储提供了可能。当考虑到具有现实抑制程度的网络时，这种机制更加明显。因此，突触缩放与可塑性的结合可能是学习结构化行为的基础，即使在最初随机的网络中也是如此。
