1. Overview
2. Prolegomena of Neural-Symbolic Computation
2.1 Principles of Neural-Symbolic Integration
2.2 Mechanisms of Neural-Symbolic Integration
2.3 Applications of Neural-Symbolic Integration
2.4 Neural-Symbolic Integration in a Nutshell
3. NSCA as Application Example for Neural-Symbolic Computing
3.1 Principles of Neural-Symbolic Integration Exemplified
3.2 Mechanisms of Neural-Symbolic Integration Exemplified
3.3 Applications of Neural-Symbolic Integration Exemplified
3.4 NSCA in a Nutshell
4. Neural-Symbolic Integration in and for Cognitive Science: Building Mental Models
4.1 Cognitive Neuroscience and Neural-Symbolic Methods
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
6.1 Feasibility of the First-Order Core Method and the Embedding of First-Order Rules into Neural Networks
6.2 Learning in a First-Order Setting
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
9.1 Other Paradigms in Computation and Representation Narrowing the Neural-Symbolic Gap
9.2 Other Application Systems Narrowing the Neural-Symbolic Gap
9.3 Other Research Efforts and Programs Narrowing the Neural-Symbolic Gap
10. Challenges and Future Directions
11. Concluding Remarks