This review describes and motivates six principles for computational cognitive neuroscience models: biological realism, distributed representations, inhibitory competition, bidirectional activation propagation, error-driven task learning, and Hebbian model learning. Although these principles are supported by a number of cognitive, computational and biological motivations, the prototypical neural-network model (a feedforward back-propagation network) incorporates only two of them, and no widely used model incorporates all of them. It is argued here that these principles should be integrated into a coherent overall framework, and some potential synergies and conflicts in doing so are discussed.

中文翻译：

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基于生物学的皮质认知计算模型的六项原则

这篇综述描述并提出了计算认知神经科学模型的六项原则：生物现实主义、分布式表示、抑制竞争、双向激活传播、错误驱动的任务学习和赫布模型学习。尽管这些原则得到了许多认知、计算和生物动机的支持，但原型神经网络模型（前馈反向传播网络）仅包含其中的两个，并且没有广泛使用的模型包含所有这些。这里认为这些原则应该被整合到一个连贯的整体框架中，并讨论了这样做的一些潜在的协同作用和冲突。