Willshaw D J, Buckingham J T
Centre for Cognitive Science, University of Edinburgh, U.K.
Philos Trans R Soc Lond B Biol Sci. 1990 Aug 29;329(1253):205-15. doi: 10.1098/rstb.1990.0165.
The recent reawakened interest in 'neural' networks begs the question of their relevance to the analysis of real nervous systems. Network models have been criticized for the lack of realism of their individual components, and because the architectures required by some neural-network algorithms do not seem to exist in real nervous systems. In three related papers published in the 1970s, David Marr proposed that the cerebellum, the neocortex and the hippocampus each acts as a memorizing device. These theories were intended to satisfy the biological constraints, but in computational terms they are undetermined. In this paper we reassess Marr's theory of the hippocampus as a temporary memory store. We give a complete computational account of the theory and we show that Marr's computational arguments do not sufficiently constrain his choice of model. We discuss Marr's specific model of temporary memory with reference to the neurophysiology and neuroanatomy of the mammalian hippocampus. Our analysis is supported by simulation studies done on various memory models built according to the principles advocated by Marr.
最近对“神经网络”重新燃起的兴趣引发了一个问题，即它们与真实神经系统分析的相关性。网络模型因其单个组件缺乏现实性而受到批评，并且因为一些神经网络算法所需的架构似乎在真实神经系统中并不存在。在20世纪70年代发表的三篇相关论文中，大卫·马尔提出小脑、新皮层和海马体各自充当记忆装置。这些理论旨在满足生物学限制，但从计算角度来看它们并不确定。在本文中，我们重新评估马尔关于海马体作为临时记忆存储的理论。我们给出了该理论完整的计算说明，并表明马尔的计算论证不足以约束他对模型的选择。我们参照哺乳动物海马体的神经生理学和神经解剖学来讨论马尔的临时记忆特定模型。我们的分析得到了根据马尔所倡导的原则构建的各种记忆模型的模拟研究的支持。
