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Department of Neurobiology   Dept. of Neurobiology, Bielefeld University


Bielefeld University Library   Bielefeld University Library


   University Library Center NRW, Germany    University Library Center NRW, Germany


Contents
show floating TOC
Preface to the 1st edition
Preface to the 2nd edition
9 Massively Parallel Systems
10 Feedforward Networks
10.1 Spatial Low-Pass Filter
10.2 Spatial High-Pass Filter
10.3 Spatial Band-Pass Filter
Box 4
10.4 A Winner-Take-All Network
10.5 Learning Matrix
10.6 Perceptron
10.7 Multilayered Feedforward Nets
11 Recurrent Networks
11.1 Pattern Generation
Box 5
11.2 Recurrent Winner-Take-All Network
11.3 Optimization
11.4 Associative Storage
11.5 Dynamic Networks
Box 6
12 Learning
12.1 Hebb's Rule
12.2 The Delta Rule
12.3 Generalized Delta Rule
12.4 Learning with IC Units
12.5 Reinforcement Learning
12.6 Expert Networks
12.7 Genetic Algorithms
12.8 Competitive Learning
13 Feature Maps
Box 7
14 Some Special Recurrent Networks
14.1 Elman Nets and Jordan Nets
14.2 Echo State Networks
14.3 Linear differential equations and recurrent neural networks
14.4 MMC Nets
14.5 Forward Models and Inverse Models
15 Spatial Coding
16 Animats
17 Development of Complexity in Animats
Box 8
18 Emergent Properties
19 The Internal Aspect − Might Machines Have Feelings?
References
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