Connectionism: A Handson ApproachISBN: 9781405130745
208 pages
April 2005, WileyBlackwell

 explores three different types of connectionist architectures
– distributed associative memory, perceptron, and multilayer
perceptron
 provides a brief overview of each architecture, a detailed
introduction on how to use a program to explore this network, and a
series of practical exercises that are designed to highlight the
advantages, and disadvantages, of each
 accompanied by a website at
http://www.bcp.psych.ualberta.ca/~mike/Book3/ that includes
practice exercises and software, as well as the files and blank
exercise sheets required for performing the exercises
 designed to be used as a standalone volume or alongside Minds and Machines: Connectionism and Psychological Modeling (by Michael R.W. Dawson, Blackwell 2004)
1.1 Connectionism In Principle And In Practice.
1.2 The Organization Of This Book.
2. The Distributed Associative Memory.
2.1 The Paired Associates Task.
2.2 The Standard Pattern Associator.
2.3 Exploring The Distributed associative memory.
3. The James Program.
3.1 Introduction.
3.2 Installing The Program.
3.3 Teaching A Distributed Memory.
3.4 Testing What The Memory Has Learned.
3.5 Using The Program.
4. Introducing Hebb Learning.
4.1 Overview Of The Exercises.
4.2 Hebb Learning Of Basis Vectors.
4.3 Hebb Learning Of Orthonormal, NonBasis Vectors.
5. Limitations Of Hebb Learning.
5.1 Introduction.
5.2 The Effect Of Repetition.
5.3 The Effect Of Correlation.
6. Introducing The Delta Rule.
6.1 Introduction.
6.2 The Delta Rule.
6.3 The Delta Rule And The Effect Of Repetition.
6.4 The Delta Rule And The Effect Of Correlation.
7. Distributed Networks And Human Memory.
7.1 Background On The Paired Associate Paradigm.
7.2 The Effect Of Similarity On The Distributed Associative Memory.
8. Limitations Of Delta Rule Learning.
8.1 Introduction.
8.2 The Delta Rule And Linear Dependency.
9. The Perceptron.
9.1 Introduction.
9.2 The Limits Of Distributed Associative Memories, And Beyond.
9.3 Properties Of The Perceptron.
9.4 What Comes Next.
10. The Rosenblatt Program.
10.1 Introduction.
10.2 Installing The Program.
10.3 Training A Perceptron.
10.4 Testing What The Memory Has Learned.
11. Perceptrons And Logic Gates.
11.1 Introduction.
11.2 Boolean Algebra.
11.3 Perceptrons And TwoValued Algebra.
12. Performing More Logic With Perceptrons.
12.1 TwoValued Algebra And Pattern Spaces.
12.2 Perceptrons And Linear Separability.
12.3 Appendix Concerning The DawsonJots Font.
13. Value Units And Linear Nonseparability.
13.1 Linear Separability And Its Implications.
13.2 Value Units And The ExclusiveOr Relation.
13.3 Value Units And Connectedness.
14. Network By Problem Type Interactions.
14.1 All Networks Were Not Created Equally.
14.2 Value Units And The TwoValued Algebra.
15. Perceptrons And Generalization.
15.1 Background.
15.2 Generalization And Savings For The 9Majority Problem.
16. Animal Learning Theory And Perceptrons.
16.1 Discrimination Learning.
16.2 Linearly Separable Versions Of Patterning.
17. The Multilayer Perceptron.
17.1 Creating Sequences Of Logical Operations.
17.2 Multilayer Perceptrons And The Credit Assignment Problem.
17.3 The Implications Of The Generalized Delta Rule.
18. The Rumelhart Program.
18.1 Introduction.
18.2 Installing The Program.
18.3 Training A Multilayer Perceptron.
18.4 Testing What The Network Has Learned.
19. Beyond The Perceptron’s Limits.
19.1 Introduction.
19.2 The Generalized Delta Rule And ExclusiveOr.
20. Symmetry As A Second Case Study.
20.1 Background.
20.2 Solving Symmetry Problems With Multilayer Perceptrons.
21. How Many Hidden Units?.
21.1 Background.
21.2 How Many Hidden Value Units Are Required For 5Bit Parity?.
22. Scaling Up With The Parity Problem.
22.1 Overview Of The Exercises.
22.2 Background.
22.3 Exploring The Parity Problem.
23. Selectionism And Parity.
23.1 Background.
23.2 From Connectionism To Selectionism.
24. Interpreting A Small Network.
24.1 Background.
24.2 A Small Network.
24.3 Interpreting This Small Network.
25. Interpreting Networks Of Value Units.
25.1 Background.
25.2 Banding In The First Monks Problem.
25.3 Definite Features In The First Monks Problem.
26. Interpreting Distributed Representations.
26.1 Background.
26.2 Interpreting A 5Parity Network.
27. Creating Your Own Training Sets.
27.1 Background.
27.2 Designing And Building A Training Set.
References.
 explores three different types of connectionist architectures
– distributed associative memory, perceptron, and multilayer
perceptron
 provides a brief overview of each architecture, a detailed
introduction on how to use a program to explore this network, and a
series of practical exercises that are designed to highlight the
advantages, and disadvantages, of each
 accompanied by a website at
http://www.bcp.psych.ualberta.ca/~mike/Book3/ that includes
practice exercises and software, as well as the files and blank
exercise sheets required for performing the exercises
 designed to be used as a standalone volume or alongside Minds and Machines: Connectionism and Psychological Modeling (by Michael R.W. Dawson, Blackwell 2004)
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