Introduction to Connectionist Modelling of Cognitive Processes
Peter McLeod, 1998


Chapter 1. The Basics of Connectionist Information Processing

Chapter 2. The Attraction of Parallel Distributed Processing for Modelling Cognition

Chapter 3. Pattern Association

Chapter 4. Autoassociation

Chapter 5. Training a Multi-layer Network with an Error Signal: Hidden Units and Backpropogation

Chapter 6. Competitive Networks

Chapter 7. Recurrent Networks

Controlling sequences with an associative chain
Controlling sequences with a recurrent net
Simple recurrent networks (SRNs)
A recurrent network has the ability to settle into a particular state for a variety of different input patterns. A network connected in this manner is referred to as an attractor .

Learning sequences with tlearn

Chapter 8. Reading Aloud

Chapter 9. Language Acquisition

Chapter 10. Connectionism and Cognitive Development

Chapter 11. Connectionist Neuropsychology - Lesioning Networks

Chapter 12. Mental Representation: Rules, Symbols and Connectionist Networks

Chapter 13. Network Models of Brain Function

Memory formation in the hippocampus
This part of the chapter describes the connections to and from the hippocampus and the role that brain region plays in the formation of memory for events. The neural architecture of several subsections of the hippocampus is presented, with schematic drawings showing the basic neuron types and their interconnections. Finally, a hypothetical neural network is proposed which would display behavior appropriate for this brain region.
Invariant visual pattern recognition in the inferior temporal cortex
The second part of the chapter describes a portion of the mammalian visual system, again showing some of the neural architecture and interconnections and a neural network which displays the appropriate behavior in its ability to recognize faces when presented with various views of the faces showing differing facial expressions.

Chapter 14. Evolutionary Connectionism

Chapter 15. A Selective History of Connectionism Before 1986

Appendix 1. Installation Procedures for tlearn

Appendix 2. An Introduction to Linear Algebra for Neural Networks

Appendix 3. User Manual for tlearn

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