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Abstract：
The field of computational cognitive neuroscience (CCN) builds and tests neurobiologically detailed computational models that account for both behavioral and neuroscience data. This article leverages a key advantage of CCN-namely, that it should be possible to interface different CCN models in a plug-and-play fashion-to produce a new and biologically detailed model of perceptual category learning. The new model was created from two existing CCN models: the HMAX model of visual object processing and the COVIS model of category learning. Using bitmap images as inputs and by adjusting only a couple of learning-rate parameters, the new HMAX/COVIS model provides impressively good fits to human category-learning data from two qualitatively different experiments that used different types of category structures and different types of visual stimuli. Overall, the model provides a comprehensive neural and behavioral account of basal ganglia-mediated learning.
摘要：
计算认知神经科学（CCN）领域构建和测试神经生物学详细的计算模型，这些模型同时考虑了行为和神经科学数据。本文利用了CCN的一个关键优势-即，应该有可能以即插即用的方式连接不同的CCN模型，以产生一种新的生物学详细的感知类别学习模型。新模型是从两个现有的CCN模型中创建的：视觉对象处理的HMAX模型和类别学习的COVIS模型。使用位图图像作为输入，只调整几个学习率参数，新的HMAX/COVIS模型提供了令人印象深刻的良好拟合人类类别学习数据来自两个定性不同的实验，使用不同类型的类别结构和不同类型的视觉刺激。总的来说,该模型提供了基底神经节介导的学习的全面的神经和行为说明。
