1. Overview  
2. Prolegomena of Neural-Symbolic Computation  
3. NSCA as Application Example for Neural-Symbolic Computing  
4. Neural-Symbolic Integration in and for Cognitive Science: Building Mental Models  
5. Putting the Machinery to Work: Binding and First-Order Inference in a Neural-Symbolic Framework  
6. Connectionist First-Order Logic Learning Using the Core Method  
7. Markov Logic Networks Combining Probabilities and First-Order Logic  
8. Relating Neural-Symbolic Systems to Human-Level Artificial Intelligence  
9. (Most) Recent Developments and Work in Progress from the Neural-Symbolic Neighborhood  
10. Challenges and Future Directions  
11. Concluding Remarks  