Proposing an Artificial Neural Networks-based Model to Simulate Human Visual Cortex

Deep learning, a subfield of computer science, employs artificial neural networks inspired by the human brain to analyze complex signals and extract meaningful patterns. Although these networks share biological principles with neural systems, they are primarily used in industrial and clinical applications. This research uses artificial neural networks to model brain‑like interactions under different conditions. First, convolutional neural networks(CNNs) are used to model the visual cortex, illustrating visual feature extraction and image recall. The model then simulates migraine disorders, demonstrating neural responses such as photophobia. Finally, Hopfield networks extend the model to show how audio cues can improve image recall. The results suggest that the proposed biologically inspired framework offers a clearer understanding of interactions within the human brain. The proposed framework, grounded in biological and neurological principles, provides a biologically inspired perspective on brain interactions and offers a promising tool for understanding neural mechanisms through computational modeling.