Although deep learning has solved difficult problems in visual pattern recognition, it is mostly successful in tasks where there are lots of labeled training data available. Furthermore, the global back-propagation based training rule and the amount of employed layers represents a departure from biological inspiration. The brain is able to perform most of these tasks in a very general way from limited to no labeled data. For these reasons it is still a key research question to look into computational principles in the brain that can help guide models to unsupervisedly learn good representations which can then be used to perform tasks like classification. In this work we explore some of these principles to generate such representations for the MNIST data set. We compare the obtained results with similar recent works and verify extremely competitive results.

中文翻译：

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使用受大脑启发的原理无监督地学习用于视觉模式识别的良好表示形式

尽管深度学习解决了视觉模式识别中的难题，但在有大量标记培训数据可用的任务中，深度学习通常是成功的。此外，基于全局反向传播的训练规则和所采用的层数表示背离了生物学灵感。大脑能够以非常通用的方式执行所有这些任务，从有限的数据到没有标记的数据。由于这些原因，研究大脑中的计算原理仍然是一个关键的研究问题，它可以帮助指导模型无监督地学习良好的表示形式，然后将其用于执行诸如分类之类的任务。在这项工作中，我们探索了其中一些原理来为MNIST数据集生成此类表示。