Introduction to connectionist modelling of cognitive processes:
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Oxford [u.a.]
Oxford Univ. Press
1998
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XV, 388 S. Ill., graph. Darst. 1 Diskette (3.5'') |
| ISBN: | 0198524277 0198524269 |
Staff View
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| 100 | 1 | |a MacLeod, Peter |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Introduction to connectionist modelling of cognitive processes |c Peter McLeod ; Kim Plunkett and Edmund T. Rolls |
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Record in the Search Index
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| adam_text | Titel: Introduction to connectionist modelling of cognitive processes
Autor: MacLeod, Peter
Jahr: 1998
Contents
Prologue l
What is the problem? 1
x What connectionist models can do 2
Part I Principles
1 The basics of connectionist information processing 8
Neurally inspired information processing 8
Five assumptions about computation in the brain on which connectionist
models are based 11
Symbols and elementary equations 15
. Connectionism in a nutshell 20
Exercises with tlearn 21
2 The attraction of parallel distributed processing for
modelling cognition 30
A The representation of knowledge in connectionist networks is distributed 31
Distributed representations are damage resistant and fault tolerant 32
--^Connectionist networks allow memory access by content 34
Retrieving information from a distributed database 35
Constraint satisfaction in connectionist networks 45
There is no distinction between memory1 and processing in
connectionist models 48
Problems for distributed representations 49
3 Pattern association 51
The architecture and operation of a pattern associator 51
A pattern association network 52
The Hebb rule 54
Learning with the Hebb rule 54
Recall from a Hebb trained matrix 56
Learning different associations on the same weight matrix 56
Recall reflects the similarity of retrieval pattern and stored patterns 59
Properties of pattern associators 61
Generalisation 61
Fault tolerance 61
The importance of distributed representations for pattern associators 62
Prototype extraction and noise removal 63
Speed 63
Interference is not necessarily a bad thing 64
Further reading 65
Training a pattern associator with tlearn 65
4 Autoassociation 72
The architecture and operation of an autoassociator 72
Architecture 72
Learning with the Delta rule 74
Properties of autoassociator memories 75
What an autoassociator learns 76
Storage of different memories on the same connections 78
Pattern completion 80
Noise resistance 81
Forming categories and prototypes from individual experiences 83
Discovering a prototype from exemplars with an autoassociator 84
Learning different prototypes on the same matrix 87
Further reading 88
Autoassociation exercises with tlearn 88
5 Training a multi-layer network with an error signal:
hidden units and backpropagation 96
The perceptron convergence rule 91
Gradient descent 99
Gradient descent with a sigmoid activation function 103
Linear separability 105
Solving the XOR problem with hidden units 107
Hidden units and internal representation 108
Hinton s family tree problem 108
What the hidden units represent in the family tree task 110
Backpropagation 112
The problem 113
An informal account 114
Local minima 114
Backpropagation and biological plausibility 116
Exercises: learning Exclusive OR with tlearn 117
6 Competitive networks 127
The architecture and operation of a competitive network 128
Excitation 128
Competition 129
Weight adjustment 129
Limiting weight growth 132
Competitive learning in the brain 133
Pattern classification 136
Correlated teaching 137
Further reading 138
7 Recurrent networks 139
Controlling sequences with an associative chain 139
Controlling sequences with a recurrent net 140
State units and plan units 141
Simple recurrent networks (SRNs) 142
Learning to predict the next sound in a sequence 144
Attractors 145
Learning sequences with tlearn 148
Part II Applications
8 Reading aloud 155
The traditional 2-route model of reading aloud 156
The connectionist approach 157
The Seidenberg and McClelland model of reading aloud 158
Replicating the results of word naming experiments 161
What has the model learnt? 165
Limitations of the model 166
The Plaut, McClelland, Seidenberg and Patterson model 167
Input coding 167
Pronunciation 168
Reading with an attractor network 169
Componential attr actors 171
What have these models achieved? 171
Further reading 172
Reading aloud with tlearn 172
9 Language acquisition 178
Learning the English past tense 179
A symbolic account of past tense learning 180
A connectionist account of past tense learning 183
Early lexical development 187
A connectionist model of early lexical development 188
Evaluation of the model 192
The acquisition of syntax 194
Further reading 202
Learning the English past tense with tlearn 202
10 Connectionism and cognitive development 210
A Stages in development—a challenge for connectionism? 210
The development of object permanence 212
Modelling the development of representations which could produce
object permanence 212
Evaluating the model 218
The balance beam problem 219
Modelling the balance beam problem 222
Running the model 224
Evaluating the model 231
Stage-like behaviour from continuous change 234
Variability in learning 234
Individual differences 235
Critical periods 238
Further reading 240
Modelling the balance beam problem with tlearn 240
11 Connectionist neuropsychology—lesioning
networks 243
The simulation of deep dyslexia 245
Hinton and Shallice (1991) 246
Attr actors 248
Attractor basins 249
Lesioning an attractor 250
Is the result dependent on fine details of the model ? 251
The interpretation of double dissociation 254
Modelling a deficit in semantic memory 255
Modality and category specificity in semantic memory 255
Modelling an asymmetrical double dissociation 258
Modelling an information processing deficit in schizophrenia 260
Selective attention 260
Modelling the Stroop task 262
Lesioning the model 263
Further reading 265
Exercises with tlearn 265
12 Mental representation: rules, symbols and
connectionist networks 268
Learning minority default rules 268
Default mapping 269
Minority defaults 270
Symbols and distributed representations 273
Representing mental types 274
Symbolic attractors 275
Levels of explanation 276
Further reading 277
13 Network models of brain function 278
Memory formation in the hippocampus 278
The role of the hippocampus in memory formation 279
Information flow to and from the hippocampus 280
The internal structure of the hippocampus 281
A computational theory of hippocampal operation 283
A neural network simulation of hippocampal operation 286
The performance measure 287
Running the model 288
Performance of the network 288
Invariant visual pattern recognition in the inferior temporal cortex 292
How not to achieve position invariant object recognition 292
The flow of visual information from retina to temporal lobe 294
VisNet—an approach to biologically plausible visual object
identification 294
Testing the network 297
The importance of the trace rule for forming invariant representations 299
Brains, networks and biological plausibility 300
Further reading 302
14 Evolutionary connectionism 303
The evolution of goal directed behaviour 304
The evolutionary advantage of the capacity to learn 306
Innately guided learning in speech perception 308
The Nakisa and Plunkett model 309
Network training and evolution 310
Speed 311
Cross-linguistic performance 311
Categorical perception 312
Nativism or constructivism ? 313
15 A selective history of connectionism before 1986 314
McCulloch and Pitts (1943) 314
Logical operations with neuron-like computational units 314
Computing AND, OR and NOT 315
Producing the sensations of hot and cold 316
Hebb(1949) 318
Neuronal inspiration in psychological modelling 318
The Hebb synapse 319
Rosenblatt (1958)—the perceptron 320
Minsky and Papert (1969)—a critique of perceptrons 323
The XOR problem and the perception of connectedness 323
Hinton and Anderson (1981) 325
Hopfield(1982) 326
Content-addressable memory in networks with attractor states 326
Input patterns and energy 327
Novel inputs produce higher values of E than memories 329
Changes in state lead to a reduction in E 329
Appendix 1 Installation procedures for tlearn 331
Appendix 2 An introduction to linear algebra for neural
networks 333
Appendix 3 User manual for tlearn 340
Bibliography 376
Index 383
|
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| spelling | MacLeod, Peter Verfasser aut Introduction to connectionist modelling of cognitive processes Peter McLeod ; Kim Plunkett and Edmund T. Rolls Oxford [u.a.] Oxford Univ. Press 1998 XV, 388 S. Ill., graph. Darst. 1 Diskette (3.5'') txt rdacontent n rdamedia nc rdacarrier Konnektionismus (DE-588)4265446-4 gnd rswk-swf Kognitiver Prozess (DE-588)4140177-3 gnd rswk-swf Kognitiver Prozess (DE-588)4140177-3 s Konnektionismus (DE-588)4265446-4 s DE-604 Plunkett, Kim Verfasser aut Rolls, Edmund T. Verfasser aut HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=008039061&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | MacLeod, Peter Plunkett, Kim Rolls, Edmund T. Introduction to connectionist modelling of cognitive processes Konnektionismus (DE-588)4265446-4 gnd Kognitiver Prozess (DE-588)4140177-3 gnd |
| subject_GND | (DE-588)4265446-4 (DE-588)4140177-3 |
| title | Introduction to connectionist modelling of cognitive processes |
| title_auth | Introduction to connectionist modelling of cognitive processes |
| title_exact_search | Introduction to connectionist modelling of cognitive processes |
| title_full | Introduction to connectionist modelling of cognitive processes Peter McLeod ; Kim Plunkett and Edmund T. Rolls |
| title_fullStr | Introduction to connectionist modelling of cognitive processes Peter McLeod ; Kim Plunkett and Edmund T. Rolls |
| title_full_unstemmed | Introduction to connectionist modelling of cognitive processes Peter McLeod ; Kim Plunkett and Edmund T. Rolls |
| title_short | Introduction to connectionist modelling of cognitive processes |
| title_sort | introduction to connectionist modelling of cognitive processes |
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| topic_facet | Konnektionismus Kognitiver Prozess |
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