Biological Plausibility

Biological plausibility in artificial intelligence focuses on designing and evaluating machine learning models that mimic the structure and function of biological systems, particularly the brain. Current research emphasizes developing biologically inspired learning algorithms (e.g., Hebbian learning, spike-timing-dependent plasticity) and assessing their performance against benchmarks derived from human perception and brain activity, often using metrics like Psychophysical-Score and Brain-Score. This pursuit aims to improve model interpretability, robustness, and efficiency, while also advancing our understanding of biological neural networks. Ultimately, this research could lead to more effective and human-like AI systems with applications across various fields, including healthcare and computer vision.