Tyrrell T, Willshaw D
Centre for Cognitive Science, University of Edinburgh, U.K.
Philos Trans R Soc Lond B Biol Sci. 1992 May 29;336(1277):239-57. doi: 10.1098/rstb.1992.0059.
Marr's theory of the cerebellar cortex as an associative learning device is one of the best examples of a theory that directly relates the function of a neural system to its neural structure. However, although he assigned a precise function to each of the identified cell types of the cerebellar cortex, many of the crucial aspects of the implementation of his theory remained unspecified. We attempted to resolve these difficulties by constructing a computer simulation which contained a direct representation of the 13,000 mossy fibres and the 200,000 granule cells associated with a single Purkinje cell of the cerebellar cortex, together with the supporting Golgi, basket and stellate cells. In this paper we present a detailed explanation of Marr's theory based upon an analogy between Marr's cerebellar model and an abstract model called the associative net. Although some of Marr's assumptions contravene neuroanatomical findings, we found that in general terms his conclusion that each Purkinje cell can learn to respond to a large number of different patterns of activity in the mossy fibres is substantially correct. However, we found that this system has a lower capacity and acts more stochastically than he envisaged. The biologically realistic simulated structure that we designed can be used to assess the computational capabilities of other network theories of the cerebellum.
马尔将小脑皮质视为一种联想学习装置的理论，是将神经系统功能与其神经结构直接联系起来的理论中最好的例子之一。然而，尽管他为小脑皮质中每种已识别的细胞类型都赋予了精确的功能，但他的理论实施中的许多关键方面仍未明确。我们试图通过构建一个计算机模拟来解决这些难题，该模拟直接呈现了与小脑皮质单个浦肯野细胞相关的13000条苔藓纤维和200000个颗粒细胞，以及起支持作用的高尔基细胞、篮状细胞和星状细胞。在本文中，我们基于马尔的小脑模型与一个名为联想网络的抽象模型之间的类比，对马尔的理论进行了详细解释。尽管马尔的一些假设与神经解剖学发现相悖，但我们发现，总体而言，他关于每个浦肯野细胞能够学会对苔藓纤维中大量不同活动模式做出反应的结论基本正确。然而，我们发现这个系统的能力较低，且比他设想的更具随机性。我们设计的具有生物学现实意义的模拟结构，可用于评估其他小脑网络理论的计算能力。
