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Cortical minicolumns are considered a model of cortical organization. Theirfunction is still a source of research and not reflected properly in modernarchitecture of nets in algorithms of Artificial Intelligence. We assume itsfunction and describe it in this article. Furthermore, we show how thisproposal allows to construct a new architecture, that is not based onconvolutional neural networks, test it on MNIST data and receive close toConvolutional Neural Network accuracy. We also show that the proposedarchitecture possesses an ability to train on a small quantity of samples. Toachieve these results, we enable the minicolumns to remember contexttransformations.

An Artificial Neural Network Architecture Based on Context Transformations in Cortical Minicolumns