EXPLORING THE COMPUTATIONAL PRINCIPLES OF CONVOLUTIONAL NEURAL NETWORKS: COMPARISONS WITH BIOLOGICAL BRAINS

Abstract

This article explores the computational principles underlying Convolutional Neural Networks (CNNs) and compares them with the human visual system. CNNs, widely used in image and video recognition, are inspired by the hierarchical processing of the brain’s visual cortex, employing techniques like convolution and pooling to identify features in data. The article examines how CNNs and the human brain both rely on hierarchical processing but differ significantly in learning mechanisms, feedback systems, and adaptability. While CNNs use supervised learning and backpropagation, the brain employs more flexible processes like synaptic plasticity and feedback loops, allowing for greater adaptability and generalization. The article also discusses advancements in attention mechanisms and transfer learning within CNNs, highlighting their limitations compared to human cognition. Furthermore, ethical considerations and the need for transparency in CNNs are addressed, emphasizing the potential for biases in AI systems. Overall, the article underscores the importance of understanding these differences to advance AI development, aiming to create neural networks that are more adaptive, efficient, and ethically sound, closer to the capabilities of the human brain.