- When to use what activation function in backprop.
- Second-order extensions of backpropagation.
- Applying nonlinear optimization techniques to training networks.
- Recurrent extensions of backpropagation.
- Hebbian learning and self-organization.
- Neuron as a principal component analyzer (Ch 8).
- Recent developments in self-organizing feature maps.
- Mathematical analysis of self-organizing feature maps.
- Feature extraction with neural networks.
- Theory of Radial Basis Function networks (Ch 5).
- Information-theoretic learning algorithms (Ch 10).
- Neural network learning vs. regression algorithms.
- Developing networks with genetic algorithms.
- Support vector machines (Ch 6).
- Learning algorithms that grow and delete units.
- Learning theory.
- Nonlinear dynamics and chaos in neural nets (Ch 14).
- Neural information processing based on
- neural oscillators.
- population oscillations.
- synchronized firing.

- Neural networks and fuzzy systems.
- Modular neural networks.
- Committees of neural networks (Ch 7).
- Policy iteration methods of reinforcement learning.
- Coping with hidden states in reinforcement learning.
- The theory of reinforcement learning.
- Methods for evolving neural network topologies.
- Combinations of evolution and learning.
- Evolving neural networks for robot control.
- Evolving neural networks for game playing.
- Comparing neural nets and
- statistical methods.
- machine learning.
- symbolic reasoning.
- symbolic knowledge representation.
- mathematical optimization.

- Super-Turing neural nets.
- Performance evaluation of neural nets.
- Benchmarking for learning algorithms.
- Learning and representing structure.
- Localist models in cognitive science.
- Marker passing models in cognitive science.
- Representing high-level knowledge in neural nets.
- Symbolic and sequential reasoning in neural nets.
- The connectionist -- symbolic debate.
- Hybrid symbolic-connectionist systems.
- Extracting knowledge from a network by
- clustering methods.
- principal component analysis.
- eyeballing.
- magic.

- Neural network models of
- low-level vision.
- high-level vision.
- episodic memory.
- hippocampus.
- human category learning.
- aphasia, schizophrenia etc.
- low-level neural processes.
- the lexical system.
- consciousness.

- Neural network applications on
- speech recognition.
- hand-written character recognition.
- financial forecasting.
- time-series analysis.
- control.
- expert systems.
- natural language understanding.
- machine translation.
- game playing.
- optimization.
- performance optimization in computer systems.
- fault diagnosis in computer systems.

- Evolutionary optimization of
- 3D printing (process and objects)
- Quantum computer algorithms
- Computer architecture
- Tests for hardware and software

- Implementation of neural nets in
- optical hardware.
- electronic hardware.
- molecular hardware.
- magnetic spin chip.
- wetware cultures connected with chips.