Deep Learning and Brain Science: Modeling Neural Mechanisms to Enhance Learning and Memory in Education

Abstract

Deep learning is based on a brain-inspired model of learning and memory. Why are they essential? Deep learning architectures are based on how the brain works and how it retains information (using mechanisms like synaptic plasticity, spike-timing-dependent plasticity (STDP), and Hierarchical processing). These models connect cognitive neuroscience with educational technology, allowing educators to tailor learning modules to how students mentally process information. Neural principles such as Hebbian learning and attention are integrated into artificial intelligence to spur innovations around knowledge retention, memory consolidation, and adaptive tutoring systems. In addition, neuromorphic computing offers energy-efficient architectures for online feedback in educational systems. The potential of this intersectional approach to revolutionize education, promote sustainable learning, and improve accessibility and engagement cannot be overstated. With the help of deep learning and brain-inspired approaches, educational technology can transform how we teach and learn on a large scale