Computational neuroscience models of vision and neural network models for object recognition are often framed by different research agendas. Computational neuroscience mainly aims at replicating experimental data, while (artificial) neural networks target high performance on classification tasks. However, we propose that models of vision should be validated on object recognition tasks. At some point, mechanisms of realistic neuro-computational models of the visual cortex have to convince in object recognition as well.

In order to foster this idea, we report the recognition accuracy for two different neuro-computational models of the visual cortex on several object recognition datasets. The models were trained using unsupervised Hebbian learning rules on natural scene inputs for the emergence of receptive fields comparable to their biological counterpart. We assume that the emerged receptive fields result in a general codebook of features, which should be applicable to a variety of visual scenes.

We report the performances on datasets with different levels of difficulty, ranging from the simple MNIST to the more complex CIFAR-10 or ETH-80. We found that both networks show good results on simple digit recognition, comparable with previously published biologically plausible models. We also observed that our deeper layer neurons provide for naturalistic datasets a better recognition codebook. As for most datasets, recognition results of biologically grounded models are not available yet, our results provide a broad basis of performance values to compare methodologically similar models.

视觉的计算神经科学模型和用于物体识别的神经网络模型通常由不同的研究议程构成。计算神经科学主要针对复制实验数据，而（人工）神经网络则针对分类任务的高性能。但是，我们建议应该在对象识别任务上验证视觉模型。在某些时候，视觉皮层的真实神经计算模型的机制也必须在物体识别中令人信服。

为了培养这个想法，我们报告了视觉皮层的两种不同神经计算模型在几个对象识别数据集上的识别准确率。这些模型是使用无监督 Hebbian 学习规则对自然场景输入进行训练的，以产生与生物对应物相当的感受野。我们假设出现的感受野会产生一个通用的特征码本，它应该适用于各种视觉场景。

我们报告了具有不同难度级别的数据集的性能，从简单的 MNIST 到更复杂的 CIFAR-10 或 ETH-80。我们发现这两个网络在简单的数字识别上都显示出良好的结果，与之前发表的生物学上合理的模型相当。我们还观察到，我们的更深层神经元为自然数据集提供了更好的识别码本。对于大多数数据集，生物学基础模型的识别结果尚不可用，我们的结果为比较方法上相似的模型提供了广泛的性能值基础。