From computer to brain: foundations of computational neurosience
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
New York ; Berlin ; Heidelberg ; Hong Kong ; London ; Milan ; Pa
Springer
2002
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| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 307 - 312 |
| Physical Description: | XIX, 363 S. Ill., graph. Darst. : 25 cm |
| ISBN: | 0387955283 0387955267 |
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| 245 | 1 | 0 | |a From computer to brain |b foundations of computational neurosience |c William W. Lytton |
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Record in the Search Index
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| adam_text | Contents
Foreword vii
Preface ix
Acknowledgments xi
1 Introduction 1
1.1 For whom is this book? 1
1.2 What is in the book? 2
1.3 Do I need a computer for this book? 3
Software 3
1.4 Why learn this now? 4
1.5 What is the subtext? 5
1.6 How is the book organized? 6
1 Perspectives 9
2 Computational Neuroscience and You 11
2.1 Why learn this? 11
2.2 Brain metaphors 11
2.3 Compare and contrast computer and brain 12
2.4 Origins of computer science and neuroscience 14
2.5 Levels 15
xiv Contents
Levels of organization 16
Levels of investigation 18
2.6 New engineering vs. old engineering 18
2.7 The neural code 20
2.8 The goals and methods of computational neuroscience . 22
2.9 Summary and thoughts 23
3 Basic Neuroscience 25
3.1 Why learn this? 25
3.2 Microscopic view of the nervous system 26
3.3 Macroscopic view of the nervous system 29
Slicing the brain 29
3.4 Parts of the brain 31
3.5 How do we learn about the brain? 34
Anatomical methods 35
3.6 Neurophysiology 36
3.7 Molecular biology and neuropharmacology 37
3.8 Psychophysics 38
3.9 Clinical neurology and neuropsychology 39
Ablative diseases 39
Intrinsic diseases 40
3.10 Summary and thoughts 41
II Computers 43
4 Computer Representations 45
4.1 Why learn this? 45
4.2 Calculator or typewriter 47
4.3 Punch cards and Boolean algebra 48
4.4 Analog vs. digital representations 50
4.5 Types of computer representations 51
4.6 Representation of numbers 52
Representation of letters and words 55
4.7 Representation of pictures 56
4.8 Neurospeculation 59
4.9 Summary and thoughts 62
5 The Soul of an Old Machine 63
5.1 Why learn this? 63
5.2 The art of the hack 64
5.3 Software and hardware 65
5.4 Basic computer design 66
Pointers come from computer memory design 67
Sequential algorithms come from computer control flow . 67
Contents xv
CPU: machine commands 69
5.5 Programs and hacks 69
Conditionals 71
5.6 Pointer manipulation 74
A kludge 75
A computer virus 76
5.7 Neurospeculation 77
5.8 Summary and thoughts 82
III Cybernetics 83
6 Concept Neurons 87
6.1 Why learn this? 87
6.2 History and description of McCulloch Pitts neurons ... 88
6.3 Describing networks by weights and states 90
Calculating total summed input by dot product 92
Calculating state 94
6.4 From single unit to network of units 95
6.5 Network architecture 102
6.6 Summary and thoughts 104
7 Neural Coding 105
7.1 Why learn this? 105
7.2 Coding in space: ensemble codes 106
Local vs. distributed ensemble coding 108
7.3 Coding with volts and chemicals: neural state code ... 110
7.4 Coding in time: temporal and rate codes Ill
Temporal integration 112
Clocking 113
7.5 Frequency coding 114
7.6 Summary and thoughts 118
8 Our Friend the Limulus 121
8.1 Why learn this? 121
8.2 The biology 122
8.3 What we can ignore 122
8.4 Why the eye lies: the problem 123
8.5 Design issues 126
Making the model small — scaling 126
Making the model small — dimensional reduction .... 127
Eliminating edge effects — wraparound 128
Presenting the input — parameterization 130
Parameterizing the activation function 132
Parameterizing the weight matrix 132
xvi Contents
8.6 The limulus equation 134
8.7 State calculation 135
8.8 Life as a limulus 237
8.9 Summary and thoughts 139
9 Supervised Learning: Delta Rule and Back Propagation 141
9.1 Why learn this? 141
9.2 Supervised learning 142
9.3 The delta rule 145
The energy analogy 146
The delta rule solves AND 147
9.4 Backward propagation 149
9.5 Distributed representations 151
9.6 Distributed representation in eye movement control ... 152
Design of the model 154
Results from the model: generalization 157
Exploration of the model: hidden unit analysis 159
Computer modeling vs. traditional mathematical modeling 161
9.7 Summary and thoughts 162
10 Associative Memory Networks 163
10.1 Why learn this? 163
10.2 Memories in an outer product 164
Association across a single synapse 164
The outer product of two vectors 165
Making hetero and autoassociative memories 167
Limit cycles 171
Instantaneous vs. gradual learning and recall 174
10.3 Critique of the Hopfield network 176
10.4 Summary and thoughts 177
IV Brains ^ jg
11 From Soap to Volts 189
11.1 Why learn this? 189
11.2 Basic cell design 190
11.3 Morphing soap and salt to batteries and resistors . . . . 192
11.4 Converting the RC circuit into an equation 194
Capacitance and current 195
Adding up the currents 196
11.5 Parameter dependence 198
11 fi AdvantaSes and disadvantages of numerical integration . 200
, i,ime constant and temporal summations . 201
11.7 blow potential theory 205
Contents xvii
Averaging by adding PSPs 208
11.8 Summary and thoughts 211
12 Hodgkin Huxley Model 213
12.1 Why learn this? 213
12.2 From passive to active 213
The resting membrane potential is about —70 mV .... 214
Th ! membrane is insulator, capacitor, and battery .... 214
Synaptic inputs aren t current injections 215
12.3 History of the action potential 216
Hodgkin. and Huxley 216
12.4 Tin: parallel conductance model 217
TIk circuit 217
Currents 219
Calculations 220
Where do the batteries come from? 221
12.5 Behavior of the active channels 223
Feedback systems 224
Particle duality 226
Particle dynamics 227
12.6 The particle equations 228
State variables define a state 229
12.7 Simulation . 231
12.8 Implications for signaling 233
The threshold and channel memory 233
Rate coding redux 234
12.9 Summary and thoughts 237
13 Compartment Modeling 239
13.1 Why learn this? 239
13.2 Dividing into compartments 241
Building the model 242
13.3 Chemical synapse modeling 245
Shunting inhibition 246
GABA and glutamate 248
13.4 Passive neuron model 250
Synaptic responses . 253
13.5 Buck propagating spikes and the Hebb synapse 254
13.6 Summary and thoughts 257
14 From Artificial Neural Network to Realistic Neural
Network 259
14.1 Why learn this? 259
14.2 Hopfield revisited 259
14.3 Suppression model for reducing interference 261
xviii Contents
14.4 A digression into philosophy 262
14.5 Acetylcholine has multiple effects 264
The dual matrix hypothesis 265
True confessions 266
14.6 Summary and thoughts 267
15 Neural Circuits 269
15.1 Why learn this? 269
15.2 The basic layout 269
15.3 Hippocampus 270
15.4 Thalamus 272
15.5 Cerebellum 273
15.6 Basal ganglia 275
15.7 Neocortex 277
15.8 Summary and thoughts 278
16 The Basics 281
16.1 Why learn this? 281
16.2 Units 282
Scientific notation 282
Numerical prefixes 282
Units and abbreviations 283
Unit conversions 285
Dimensional analysis 286
16.3 Binary 287
Translating back and forth 287
Addition and subtraction 289
Octal and hex 289
Boolean algebra 290
16.4 Linear algebra 290
What is algebra? Why linear? 291
Addition and subtraction 292
Dot product 292
Orthogonality 293
Outer product 293
Matrix multiplication 294
16.5 Numerical calculus 295
Infinitesimals 296
Numerical solutions 297
Mathematical symbols 299
Analytic solution to the charging curve 299
16.6 Electrical engineering 300
The three big laws: Ohm, Kirchhoff, and the other one . 301
Ohm s law 301
Capacitance 304
Contents xix
References 307
Glossary 313
Index 357
|
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| spelling | Lytton, William W. Verfasser aut From computer to brain foundations of computational neurosience William W. Lytton New York ; Berlin ; Heidelberg ; Hong Kong ; London ; Milan ; Pa Springer 2002 XIX, 363 S. Ill., graph. Darst. : 25 cm txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 307 - 312 Neurofisiologia larpcal Redes neurais larpcal Brain physiology Computational Biology methods Computational neuroscience Models, Neurological Nerve Net physiology Neural Networks (Computer) Neurons physiology Neuroinformatik (DE-588)4655105-0 gnd rswk-swf Neuroinformatik (DE-588)4655105-0 s DE-604 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=012922354&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Lytton, William W. From computer to brain foundations of computational neurosience Neurofisiologia larpcal Redes neurais larpcal Brain physiology Computational Biology methods Computational neuroscience Models, Neurological Nerve Net physiology Neural Networks (Computer) Neurons physiology Neuroinformatik (DE-588)4655105-0 gnd |
| subject_GND | (DE-588)4655105-0 |
| title | From computer to brain foundations of computational neurosience |
| title_auth | From computer to brain foundations of computational neurosience |
| title_exact_search | From computer to brain foundations of computational neurosience |
| title_full | From computer to brain foundations of computational neurosience William W. Lytton |
| title_fullStr | From computer to brain foundations of computational neurosience William W. Lytton |
| title_full_unstemmed | From computer to brain foundations of computational neurosience William W. Lytton |
| title_short | From computer to brain |
| title_sort | from computer to brain foundations of computational neurosience |
| title_sub | foundations of computational neurosience |
| topic | Neurofisiologia larpcal Redes neurais larpcal Brain physiology Computational Biology methods Computational neuroscience Models, Neurological Nerve Net physiology Neural Networks (Computer) Neurons physiology Neuroinformatik (DE-588)4655105-0 gnd |
| topic_facet | Neurofisiologia Redes neurais Brain physiology Computational Biology methods Computational neuroscience Models, Neurological Nerve Net physiology Neural Networks (Computer) Neurons physiology Neuroinformatik |
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