1. Cellular and subcellular level
1.1 Molecular, genetic knock-out models, genetic neural networks
1.2 Synaptic dynamics, diffusion, long-term potentiation (LTP) and depression (LTD)
1.3 Cellular dynamics, models, firing patterns, spike-timing dependent (STDP) learning rules
1.4 Neural Network dynamics, spiking networks, cell assemblies
1.5 Cellular automata, Boolean neural networks

2. Mesoscopic level and transitions between levels
2.1 Population dynamics, mean-field analysis
2.2 Chaotic dynamics, itinerancy
2.3 Phase transitions in excitable media, metastability, stochastic resonance
2.4 Complexity theory, computational power,
2.5 Synergetics, metastability, criticality, dissipative systems
2.6 Quantum Field Theory, symmetry breaking
2.7 Network topology, neuropercolation, graph and network dynamics
2.8 Development, artificial life

3. System level
3.1 Brain imaging dynamics,  fMRI, PET, SPECT, BOLD, Diffusion Tensor, EEG, ERP, MEG
3.2 Sensory pathways, neuromodulatory pathways, autonomous system
3.3 Sensorimotor dynamics, thalamocortical circuit, basal ganglia, brainstem and cerebellar coordination dynamics
3.4 Navigation, cognitive mapping, place cells
3.5 Action planning, selective attention, object recognition
3.6 Learning and memory, reinforcement learning, memory storage and retrieva
3.7 Decision making, language, emotions, consciousness

4. Applications
4.1 Neural Engineering, Brain-machine interfaces (BMI)
4.2 Neurocomputer, neuromorphic hardware
4.3 Neural computation, large scale networks simulation
4.4 Robotics, autonomic regulation and systems control
4.5 Behavior modification, rehabilitation, cognitive training.
4.6 Machine learning, neuroinformatics, pattern recognition, classifiers
4.7 Dynamics of psychiatric disorders, ADHD, Alzheimer, Parkinson, autism, schizophrenia, depression