Recognize that the terms "neural network", "connectionist", and "parallel distributed processing (PDP)" refer to the same types of models.
Distinguish between what the nodes and links represent in a neural network model versus what they represent in symbolic models.
Describe the main parts of a neuron.
Explain how neurons activate one another by action potentials.
Explain the "all or none principle" of neural firing.
Describe a synapse.
Describe: "dumb units", input units, output units, connection weights, activation thresholds, propagation, backpropagation.
Explain how the structure of a neural network models the characteristics of real neurons.
Describe examples of the tasks that neural networks can perform.
Describe how one pattern of connection weights can make two associations.
Describe how a connectionist model can learn the connection weights for an association.
(a model that shows the changing error rate)
Explain such characteristics of neural networks as "graceful degradation" and "spontaneous generalization".
Recognize the reason for the importance of the neural network approach.