Hopfield J J, Brody Carlos D
Department of Molecular Biology, Princeton University, Princeton, NJ 08544-1014, USA.
Proc Natl Acad Sci U S A. 2004 Jan 6;101(1):337-42. doi: 10.1073/pnas.2536316100. Epub 2003 Dec 23.
Plasticity in connections between neurons allows learning and adaptation, but it also allows noise to degrade the function of a network. Ongoing network self-repair is thus necessary. We describe a method to derive spike-timing-dependent plasticity rules for self-repair, based on the firing patterns of a functioning network. These plasticity rules for self-repair also provide the basis for unsupervised learning of new tasks. The particular plasticity rule derived for a network depends on the network and task. Here, self-repair is illustrated for a model of the mammalian olfactory system in which the computational task is that of odor recognition. In this olfactory example, the derived rule has qualitative similarity with experimental results seen in spike-timing-dependent plasticity. Unsupervised learning of new tasks by using the derived self-repair rule is demonstrated by learning to recognize new odors.
神经元之间连接的可塑性允许学习和适应，但它也会使噪声降低网络的功能。因此，持续的网络自我修复是必要的。我们描述了一种基于功能正常网络的放电模式来推导用于自我修复的尖峰时间依赖可塑性规则的方法。这些用于自我修复的可塑性规则也为新任务的无监督学习提供了基础。为网络推导的特定可塑性规则取决于网络和任务。在这里，我们以哺乳动物嗅觉系统的模型为例来说明自我修复，其中的计算任务是气味识别。在这个嗅觉示例中，推导的规则与尖峰时间依赖可塑性中观察到的实验结果在定性上相似。通过学习识别新气味，展示了使用推导的自我修复规则对新任务进行无监督学习。
