Neural network models simulate how biological neurons process information, learn patterns, and generate behavior. They form the computational backbone of artificial intelligence while providing insight into the brain’s complex organization and adaptive mechanisms. These models bridge experimental neuroscience with cognitive science and machine learning.

The Neural Network Models session investigates mathematical and computational frameworks that emulate brain dynamics, synaptic plasticity, and network connectivity. Researchers use artificial and spiking neural networks to model learning, perception, and memory formation at multiple scales.

At the Neural Network Models Conference, neuroscientists, computer scientists, and engineers discuss how biologically inspired architectures enhance both scientific understanding and AI development. Topics include predictive coding, recurrent networks, deep learning, and neuromorphic computing.

This session serves as a convergence point for theoretical and applied neuroscience, focusing on how data-driven modeling can reveal principles of cognition and intelligence. It also examines how computational models support medical research—such as predicting seizure activity or optimizing neurostimulation parameters.

Progress in Computational Neuroscience continues to demonstrate how realistic neural simulations can replicate sensory processing, decision-making, and even consciousness-like patterns.