Approximate Outline of Topics
Introduction and Overview
Biological Background

Largescale organization of the brain

Basic neurobiology of the neuron

Lateral inhibition in animal nervous systems
History and Development of Neural Network Models
Review of Linear Algebra
SingleLayer Networks
 Linear associators
 Simple perceptrons
 Perceptron convergence theorem
 Gradient descent learning
 Delta rule
MultiLayer Networks
 Multilayer perceptrons
 Backpropagation
 Variations on backpropagation
Recurrent Networks
 Hopfield networks
 Boltzmann machines and simulated annealing
 Elman networks and sequence learning
 BrainStateinaBox model
 Recurrent backpropagation
Unsupervised Networks
 Competitive learning
 Vector quantization
 Kohonen selforganizing maps
Sparse Distributed Memory
Distributed Representations of Information
 Recursive AutoAssociative Memory
 Holistic processing
 Finding structure in time
 Binary spatter coding
 Holographic Reduced Representations
Evolutionary Approaches
 Designing neural networks with genetic algorithms

Evolutionary reinforcement learning