Memory and the computational brain: why cognitive science will transform neuroscience
'Memory and the Computational Brain' offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades.
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Chichester
Wiley-Blackwell
2009
|
| Ausgabe: | 1. published |
| Schriftenreihe: | Blackwell/Maryland lectures in language and cognition
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Zusammenfassung: | 'Memory and the Computational Brain' offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. |
| Beschreibung: | XVI, 319 Seiten Illustrationen, Diagramme |
| ISBN: | 9781405122870 9781405122887 |
Internformat
MARC
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| 245 | 1 | 0 | |a Memory and the computational brain |b why cognitive science will transform neuroscience |c C. R. Gallistel and Adam Philip King |
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| 264 | 1 | |a Chichester |b Wiley-Blackwell |c 2009 | |
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| 338 | |b nc |2 rdacarrier | ||
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Datensatz im Suchindex
| _version_ | 1804140006821855232 |
|---|---|
| adam_text | Contents
Preface viii
1
Information
1
Shannon s Theory of Communication
2
Measuring Information
7
Efficient Coding
16
Information and the Brain
20
Digital and Analog Signals
24
Appendix: The Information Content of Rare Versus Common
25
Events and Signals
2
Bayesian Updating
27
Bayes
Theorem and Our Intuitions about Evidence
30
Using
Bayes
Rule
32
Summary
41
3
Functions
43
Functions of One Argument
43
Composition and Decomposition of Functions
46
Functions of More than One Argument
48
The Limits to Functional Decomposition
49
Functions Can Map to Multi-Part Outputs
49
Mapping to Multiple-Element Outputs Does Not Increase SO
Expressive Power
Defining Particular Functions
51
Summary: Physical/Neurobiological Implications of Facts
53
about Functions
4
Representations
55
Some Simple Examples
56
Notation
59
The Algebraic Representation of Geometry
64
vi
Contents
5 Symbols 72
Physical Properties of Good Symbols
72
Symbol Taxonomy
79
Summary
2
6
Procedures °5
Algorithms
85
Procedures, Computation, and Symbols
87
Coding and Procedures
89
Two Senses of Knowing
Ю0
A Geometric Example
101
7
Computation
104
Formalizing Procedures
105
The Turing Machine
107
Turing Machine for the Successor Function
110
Turing Machines for fisjvm
Ш
Turing Machines for f+
115
Minimal Memory Structure
121
General Purpose Computer
122
Summary
124
8
Architectures
126
One-Dimensional Look-Up Tables (If-Then Implementation)
128
Adding State Memory: Finite-State Machines
131
Adding Register Memory
137
Summary
144
9
Data Structures
149
Finding Information in Memory
151
An Illustrative Example
160
Procedures and the Coding of Data Structures
165
The Structure of the Read-Only Biological Memory
167
10
Computing with Neurons
170
Transducers and Conductors
171
Synapses and the Logic Gates
172
The Slowness of It All
173
The Time-Scale Problem
174
Synaptic Plasticity
175
Recurrent Loops in Which Activity Reverberates
183
11
The Nature of Learning
187
Learning As Rewiring
187
Synaptic Plasticity and the Associative Theory of Learning
189
Why Associations Are Not Symbols
191
Contents
vii
Distributed Coding
192
Learning As the Extraction and Preservation
196
of Useful Information
Updating an Estimate of One s Location
198
12
Learning Time and Space
207
Computational Accessibility
207
Learning the Time of Day
208
Learning Durations
211
Episodic Memory
213
13
The Modularity of Learning
218
Example
1:
Path Integration
219
Example
2:
Learning the Solar Ephemeris
220
Example
3:
Associative Learning
226
Summary
241
14
Dead Reckoning in a Neural Network
242
Reverberating Circuits as Read/Write Memory Mechanisms
245
Implementing Combinatorial Operations by Table-Look-Up
250
The Full Model
251
The Ontogeny of the Connections?
252
How Realistic Is the Model?
254
Lessons to Be Drawn
258
Summary
265
15
Neural Models of Interval Timing
266
Timing an Interval on First Encounter
266
Dworkin s Paradox
268
Neurally Inspired Models
269
The Deeper Problems
276
16
The Molecular Basis of Memory
278
The Need to Separate Theory of Memory from Theory of
278
Learning
The Coding Question
279
A Cautionary Tale
281
Why Not Synaptic Conductance?
282
A Molecular or Sub-Molecular Mechanism?
283
Bringing the Data to the Computational Machinery
283
Is It Universal?
286
References
288
Glossary
299
Index
312
|
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| isbn | 9781405122870 9781405122887 |
| language | English |
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| spelling | Gallistel, Charles R. 1941- Verfasser (DE-588)13899434X aut Memory and the computational brain why cognitive science will transform neuroscience C. R. Gallistel and Adam Philip King 1. published Chichester Wiley-Blackwell 2009 XVI, 319 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Blackwell/Maryland lectures in language and cognition 'Memory and the Computational Brain' offers a provocative argument that goes to the heart of neuroscience, proposing that the field can and should benefit from the recent advances of cognitive science and the development of information theory over the course of the last several decades. Cognitive neuroscience Cognitive science Kognition (DE-588)4031630-0 gnd rswk-swf Neurowissenschaften (DE-588)7555119-6 gnd rswk-swf Kognitionswissenschaft (DE-588)4193780-6 gnd rswk-swf Kognition (DE-588)4031630-0 s Neurowissenschaften (DE-588)7555119-6 s DE-604 Kognitionswissenschaft (DE-588)4193780-6 s b DE-604 King, Adam Philip Verfasser aut Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018004932&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Gallistel, Charles R. 1941- King, Adam Philip Memory and the computational brain why cognitive science will transform neuroscience Cognitive neuroscience Cognitive science Kognition (DE-588)4031630-0 gnd Neurowissenschaften (DE-588)7555119-6 gnd Kognitionswissenschaft (DE-588)4193780-6 gnd |
| subject_GND | (DE-588)4031630-0 (DE-588)7555119-6 (DE-588)4193780-6 |
| title | Memory and the computational brain why cognitive science will transform neuroscience |
| title_auth | Memory and the computational brain why cognitive science will transform neuroscience |
| title_exact_search | Memory and the computational brain why cognitive science will transform neuroscience |
| title_full | Memory and the computational brain why cognitive science will transform neuroscience C. R. Gallistel and Adam Philip King |
| title_fullStr | Memory and the computational brain why cognitive science will transform neuroscience C. R. Gallistel and Adam Philip King |
| title_full_unstemmed | Memory and the computational brain why cognitive science will transform neuroscience C. R. Gallistel and Adam Philip King |
| title_short | Memory and the computational brain |
| title_sort | memory and the computational brain why cognitive science will transform neuroscience |
| title_sub | why cognitive science will transform neuroscience |
| topic | Cognitive neuroscience Cognitive science Kognition (DE-588)4031630-0 gnd Neurowissenschaften (DE-588)7555119-6 gnd Kognitionswissenschaft (DE-588)4193780-6 gnd |
| topic_facet | Cognitive neuroscience Cognitive science Kognition Neurowissenschaften Kognitionswissenschaft |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018004932&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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