This thesis gives an insight into the connection between how human and artificial neural networks process information. We investigate key topics to fully understand the relationship between these parallel lines. We start with an introduction to the ever-evolving field of neuroscience. This is followed by a dive into the work of David Hubel and Torsten Wiesel, whose experiments helped uncover how the visual system develops in living beings. The thesis then looks into critical periods along with their link to the development of the brain and the risks of laboratory experiments. At first we establish how humans process information, and then investigate how computers do the same. We begin with insights into deep learning and its connection to neural networks. We look at the implementation of CNNs and then further establish the parallel line connection through the concept of neocognitron. Transfer learning is also defined. Finally, the thesis links together the parallel line theories between living beings and computers.
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