Stable Learning in Stochastic Network States
Stable Learning in Stochastic Network States
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DOI：
10.1523/jneurosci.2496-11.2012
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发表时间：
2012-01-04
影响因子：
5.3
通讯作者：
Destexhe, Alain
中科院分区：
文献类型：
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作者：
El Boustani, Sami;Yger, Pierre;Destexhe, Alain
哺乳动物大脑皮层的特点是在体内不规则的自发活动，但这种持续的动态如何影响信号处理和学习仍然是未知的。在体外，主要是在沉默的网络中，显示的关联可塑性规则，是基于检测突触前和突触后活动之间的相关性，因此是敏感的自发活动和虚假的相关性。因此，它们不能在现实的网络状态下运行。在这里，我们提出了一类新的尖峰定时依赖的可塑性学习规则与本地浮动可塑性阈值，其中的缓慢动态解释metaplasticity。这种新的算法被证明既正确地预测突触权重的稳态，又解决了噪声状态下的渐近稳定学习问题。它自然地包含许多其他已知类型的学习规则，将它们统一到一个连贯的框架中。浮动可塑性阈值的混合突触前和突触后依赖性是合理的级联已知的分子通路，这导致实验可检验的预测。
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.