Our recently introduced Neocortex-Inspired Locally Recurrent Neural Network is a machine learning system that is able to learn feature extraction functions from sequential data in an unsupervised way. While it was designed with the main purpose of feature learning, its structure and desired functioning is highly inspired by models of the feedforward circuits in the neocortex. In this work, we study the behavior of our system when it takes shifting images as input, and we compare it with known behavior of the primary visual cortex. The results show that some of the best-known emerging properties in the primary visual cortex, such as the emergence of simple and complex cells as well as orientation maps, also occur in our system, indicating that also their behaviors can be considered analogous. This validates our system as a potential model of the primary visual cortex that may contribute to further understanding of its functioning. In addition, considering that most areas in the neocortex show similarities in terms of structure and operation, future studies of our system over inputs other than images may also bring new insights about other neocortical areas.