1) Modeling a Neuron - artificial neural networks are modeled aver 
individual neurons

2) Representation - "Once a problem is described using an appropriate 
representation, the problem is almost solved."

3) Formal Neurons - Neurons that compute by means of a 
linear threshold

4) Learning by Artificial Neural Networks - discuses ways in which a AI 
mind can learn and be taught

5) Computation with Formal Neurons - The process of solving complex 
calculations using formal neurons.

6) Exclusive-or Network - Networks composed of three layers of units with 
arrows that point from the lower layers to the higher layers that help to 
make communication between artificial networks.

7)  Training styles - Different ways to teach something with artificial 
intelligence such as by hard-wiring or reinforcement.

8)Connectionism models - Artificial neural networks/parallel distributed 
processing.

9)Feedback and word Recognition - The units of the network are not boolean 
but take on values from an interval of real numbers that start small and 
work up to bigger problems such as recognizing letters in words first then 
identifying the word.

10)Virtues of Artificial Neural Networks - Artificial Neural Networks are 
able to produce cognitive models that are superior to symbolic models 
because of advantages in architecture.