How do computational models of neural networks help us understand the brain's information processing mechanisms, such as sensory perception, learning, memory, and decision-making? What are the challenges in building biologically realistic models that capture the complexity and dynamics of neural circuits?

Computational models of neural networks simulate the behavior of interconnected neurons to study how information is encoded, processed, and transmitted in the brain. These models provide insights into neural dynamics, plasticity, and emergent phenomena such as synchronization and oscillations. Challenges include incorporating anatomical and physiological details, reconciling data from different scales, and validating model predictions with experimental observations.