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Although deep learning has solved difficult problems in visual patternrecognition, it is mostly successful in tasks where there are lots of labeledtraining data available. Furthermore, the global back-propagation basedtraining rule and the amount of employed layers represents a departure frombiological inspiration. The brain is able to perform most of these tasks in avery general way from limited to no labeled data. For these reasons it is stilla key research question to look into computational principles in the brain thatcan help guide models to unsupervisedly learn good representations which canthen be used to perform tasks like classification. In this work we explore someof these principles to generate such representations for the MNIST data set. Wecompare the obtained results with similar recent works and verify extremelycompetitive results.

Using brain inspired principles to unsupervisedly learn good representations for visual pattern recognition