Montague P R, Sejnowski T J
Division of Neuroscience, Baylor College of Medicine, Houston, Texas 77030, USA.
Learn Mem. 1994 May-Jun;1(1):1-33.
Some forms of synaptic plasticity depend on the temporal coincidence of presynaptic activity and postsynaptic response. This requirement is consistent with the Hebbian, or correlational, type of learning rule used in many neural network models. Recent evidence suggests that synaptic plasticity may depend in part on the production of a membrane permeant-diffusible signal so that spatial volume may also be involved in correlational learning rules. This latter form of synaptic change has been called volume learning. In both Hebbian and volume learning rules, interaction among synaptic inputs depends on the degree of coincidence of the inputs and is otherwise insensitive to their exact temporal order. Conditioning experiments and psychophysical studies have shown, however, that most animals are highly sensitive to the temporal order of the sensory inputs. Although these experiments assay the behavior of the entire animal or perceptual system, they raise the possibility that nervous systems may be sensitive to temporally ordered events at many spatial and temporal scales. We suggest here the existence of a new class of learning rule, called a predictive Hebbian learning rule, that is sensitive to the temporal ordering of synaptic inputs. We show how this predictive learning rule could act at single synaptic connections and through diffuse neuromodulatory systems.
某些形式的突触可塑性取决于突触前活动和突触后反应的时间一致性。这一要求与许多神经网络模型中使用的赫布型或相关性学习规则一致。最近的证据表明，突触可塑性可能部分取决于一种可透过膜扩散的信号的产生，因此空间容积也可能参与相关性学习规则。这种后一种形式的突触变化被称为容积学习。在赫布型和容积学习规则中，突触输入之间的相互作用取决于输入的一致程度，否则对其确切的时间顺序不敏感。然而，条件实验和心理物理学研究表明，大多数动物对感觉输入的时间顺序高度敏感。尽管这些实验分析的是整个动物或感知系统的行为，但它们提出了神经系统可能在许多空间和时间尺度上对时间有序事件敏感的可能性。我们在此提出存在一种新的学习规则类别，称为预测性赫布学习规则，它对突触输入的时间顺序敏感。我们展示了这种预测性学习规则如何在单个突触连接以及通过弥散性神经调节系统起作用。
