Track 1: Neuro-Cognitive Computing and Brain-Inspired AI

• Computational neuroscience and cognitive modeling
• Brain-inspired learning algorithms
• Spiking neural networks and neural dynamics
• Neuromorphic computing architectures
• Neuro-symbolic and hybrid intelligence models
• Cognitive perception and sensory processing
• Memory, attention, and learning mechanisms
• Neuroinformatics and brain data analytics
• Brain–computer interfaces and neural signal processing

Track 2: Artificial Intelligence Algorithms and Intelligent Systems

• Machine learning and deep learning techniques
• Reinforcement learning and adaptive intelligence
• Explainable and interpretable AI models
• Knowledge representation and reasoning systems
• Multimodal and context-aware intelligence
• Autonomous and self-learning systems
• Intelligent decision support systems
• Evolutionary and swarm intelligence
• Cognitive control and planning systems

Track 3: Applications of Neuro-Cognitive AI and Intelligent Systems

• AI for healthcare diagnostics and neuro-medicine
• Cognitive robotics and human–robot interaction
• Smart environments and ambient intelligence
• Intelligent transportation and autonomous vehicles
• AI-driven education and personalized learning
• Neuro-cognitive AI in Industry 5.0
• Intelligent cybersecurity and threat detection
• Emotion-aware and affective computing applications
• AI for smart cities and sustainable systems