Unité de Neurosciences, Information et Complexité, CNRS, Unité Propre de Recherche 3293, 91198 Gif-sur-Yvette, France.
J Neurosci. 2012 Jan 4;32(1):194-214. doi: 10.1523/JNEUROSCI.2496-11.2012.
The mammalian cerebral cortex is characterized in vivo by irregular spontaneous activity, but how this ongoing dynamics affects signal processing and learning remains unknown. The associative plasticity rules demonstrated in vitro, mostly in silent networks, are based on the detection of correlations between presynaptic and postsynaptic activity and hence are sensitive to spontaneous activity and spurious correlations. Therefore, they cannot operate in realistic network states. Here, we present a new class of spike-timing-dependent plasticity learning rules with local floating plasticity thresholds, the slow dynamics of which account for metaplasticity. This novel algorithm is shown to both correctly predict homeostasis in synaptic weights and solve the problem of asymptotic stable learning in noisy states. It is shown to naturally encompass many other known types of learning rule, unifying them into a single coherent framework. The mixed presynaptic and postsynaptic dependency of the floating plasticity threshold is justified by a cascade of known molecular pathways, which leads to experimentally testable predictions.
哺乳动物大脑皮层的特征是在体内具有不规则的自发活动，但这种持续的动力学如何影响信号处理和学习仍然未知。在体外证明的联想可塑性规则，主要是在沉默网络中，基于检测突触前和突触后活动之间的相关性，因此对自发活动和虚假相关性敏感。因此，它们不能在现实网络状态下运行。在这里，我们提出了一类新的尖峰时间依赖可塑性学习规则，具有局部浮动可塑性阈值，其缓慢动力学解释了超可塑性。该新算法被证明既能正确预测突触权重的动态平衡，又能解决噪声状态下渐近稳定学习的问题。它被证明可以自然地包含许多其他已知类型的学习规则，将它们统一到一个单一的连贯框架中。浮动可塑性阈值的混合突触前和突触后依赖性由一系列已知的分子途径来证明，这导致了可进行实验测试的预测。
