(The following topics include but are not limited to)
Topic 1. Neural networks
Mathematical modeling of the nervous system
Neurodynamic analysis
Neural information processing
Neurodynamic optimization and simulation
Impulsive neural network
Neural coding and decoding mechanisms
Neural differential equation
Application of neurodynamics theory in engineering field
Deep neural networks, convolutional neural networks
Generate adversarial networks and recurrent neural networks
Neural network structure and neural symbol mixed model
Interpretability and visualization methods of neural networks
Neural networks in medical, financial, energy and other fields
Topic 2. Optimization algorithm
Linear algebra
‌ calculus
Probability theory and statistics
‌ Numerical analysis
Concrete optimization algorithm
Computability theory
Algorithmic graph theory and composition
Computational geometry
Computing technology and application
Topic 3. Deep Learning
Basic algorithms for deep learning
Deep learning in computer vision
Combining natural language processing with vision
Transfer learning
Domain adaptation
Reinforcement learning
Joint learning and privacy protection
Self-supervised and unsupervised learning
Interpretability of deep learning models
Edge computing