Neuronal adaptation theory: including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Frankfurt am Main [u.a.]
Lang
1996
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Literaturverz. S. 229 - 236 |
| Physical Description: | 236 S. Ill., graph. Darst. |
| ISBN: | 3631300395 0820431729 |
Staff View
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| 100 | 1 | |a Carmesin, Hans-Otto |e Verfasser |0 (DE-588)1211419223 |4 aut | |
| 245 | 1 | 0 | |a Neuronal adaptation theory |b including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems |c Hans-Otto Carmesin |
| 264 | 1 | |a Frankfurt am Main [u.a.] |b Lang |c 1996 | |
| 300 | |a 236 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Literaturverz. S. 229 - 236 | ||
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| _version_ | 1807320601235292160 |
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9
CONTENTS
1
INTRODUCTION
13
1.1
THE
ROLE
OF
THEORY
.
13
2
NEURONAL
ASSOCIATION
PATTERNS
17
2.1
CLASSICAL
CONDITIONING
.
17
2.2
TYPICAL
NERVE
CELL
.
18
2.3
NEURONAL
DYNAMICS
.
20
2.3.1
TWO-VALUED
NEURONS
.
20
2.3.2
TWO
ALTERNATIVE
FORMULATIONS
.
21
2.4
COUPLING
DYNAMICS
.
23
2.4.1
USAGE
DEPENDENT
COUPLINGS
.
25
2.4.2
NEURONAL
ACTIVITY
PATTERNS
.
25
2.5
NETWORK
MODEL
FOR
CLASSICAL
CONDITIONING
.
29
2.6
PATTERN
RECOGNITION
.
32
2.6.1
TASK
.
32
2.6.2
ONE
PATTERN
.
32
2.6.3
SEVERAL
PATTERNS
.
34
2.7
PATTERN
RETRIEVAL
WITH
STOCHASTIC
DYNAMICS
.
39
2.7.1
DYNAMICAL
EQUILIBRIUM
FOR
A
SINGLE
NEURON
.
40
2.7.2
DYNAMICAL
EQUILIBRIUM
FOR
CONFIGURATIONS
.
40
2.8
A
PHYSIOLOGICAL
BASIS
OF
STOCHASTIC
DYNAMICS
.
44
2.8.1
BIOPHYSICS
OF
ACTION
POTENTIALS
.
44
2.8.2
SPHERICAL
CAPACITOR
CELL
MODEL
.
45
2.8.3
NYQUIST
FORMULA
.
46
2.8.4
THERMODYNAMIC
MEMBRANE
POTENTIAL
FLUCTUATIONS
.
50
2.8.5
RESULTING
STOCHASTIC
NEURONAL
DYNAMICS
.
51
2.8.6
DISCUSSION
.
53
2.9
PATTERN
RETRIEVAL
WITH
EFFECTIVELY
CONTINUOUS
TIME
.
53
2.9.1
CONTINUOUS
SPIKE
RESPONSE
FUNCTION
.
54
2.9.2
NETWORK
MODEL
.
54
2.9.3
MODEL
ANALYSIS
.
55
2.10
DISCUSSION
OF
CHAPTER
2
.
58
3
SELF-ORGANIZING
NETWORKS
60
3.1
BASIC
PRINCIPLE
.
61
3.2
RETINOTOPY
AS
MODEL
SYSTEM
.
61
3.3
GENERAL
TWO-VALUED
NEURON
COUPLING
RULES
.
63
3.3.1
LOCALITY
PRINCIPLE
.
63
3.3.2
ADDITIVE
MEMBRANE
POTENTIAL
RULE,
AMPR
.
64
10
CONTENTS
3.3.3
COUPLING
TRANSFER
RULE,
CTR
.
64
3.3.4
LOCAL
LINEAR
COUPLING
DYNAMICS,
LLCD
.
65
3.3.5
LIMITED
NEURONAL
COUPLINGS,
LNCR
.
65
3.4
A
ID
SELF-ORGANIZING
NETWORK
WITH
HEBB-RULE
.
65
3.4.1
NETWORK
ARCHITECTURE
.
65
3.4.2
COUPLING
DYNAMICS
.
67
3.4.3
TRANSFORMED
COUPLINGS
.
68
3.4.4
SINGLE
STIMULATION
POTENTIAL
.
68
3.5
FIELD
THEORY
OF
NEUROSYNAPTIC
DYNAMICS
.
69
3.5.1
A
GENERAL
SOLUTION
METHOD
.
69
3.5.2
ERGODICITY
.
69
3.5.3
NEUROSYNAPTIC
STATES
AND
TRANSITIONS
.
70
3.5.4
AVERAGED
NEUROSYNAPTIC
CHANGE
FIELD
.
70
3.5.5
DIFFERENTIAL
EQUATION
FOR
NEUROSYNAPTIC
CHANGE
FIELD
.
71
3.5.6
ADIABATIC
PRINCIPLE
.
71
3.5.7
DIFFERENTIAL
EQUATION
FOR
SYNAPTIC
CHANGE
FIELD
.
72
3.5.8
CHANGE
POTENTIAL
FIELD
.
73
3.5.9
FLUCTUATION
DISSIPATION
THEOREMS
.
77
3.5.10
DISCUSSION
.
81
3.6
FIELD
THEORY
OF
TOPOLOGY
PRESERVATION
.
81
3.6.1
EMERGENCE
OF
AN
INJECTIVE
MAPPING
.
81
3.6.2
SINGLE
NEURON
SEPARATION
.
83
3.6.3
COINCIDENCE
STABILIZATION
.
84
3.6.4
EMERGENCE
OF
ID
TOPOLOGY
PRESERVATION
.
86
3.6.5
EMERGENCE
OF
CLUSTERS
AND
TOPOLOGY
PRESERVATION
.
87
3.6.6
DISCUSSION
.
92
3.7
FIELD
THEORY
OF
ORIENTATION
PREFERENCE
EMERGENCE
.
92
3.7.1
NETWORK
MODEL
.
92
3.7.2
CHANGE
POTENTIALS
.
94
3.7.3
POTENTIAL
MINIMA
.
95
3.7.4
DISCUSSION
.
97
3.8
FIELD
THEORY
OF
ORIENTATION
PATTERN
EMERGENCE
.
98
3.8.1
PHENOMENON
OF
PINWHEEL
STRUCTURES
.
98
3.8.2
NETWORK
MODEL
.
98
3.8.3
EFFECTIVE
ISO-ORIENTATION
INTERACTION
.
100
3.8.4
CONTINUOUS
ORIENTATION
INTERACTION
.
101
3.8.5
ORIENTATION
FLUCTUATIONS
.
'
.
102
3.8.6
INSTABILITY
OF
THE
GROUND
STATE
.
103
3.8.7
TOPOLOGICAL
SINGULARITIES
ACCORDING
TO
THE
POISSON
EQUATION
.
104
3.8.8
GREENS
FUNCTION
SOLUTION
.
106
3.8.9
ENERGY
OF
A
PLANAR
SYSTEM
OF
CHARGES
.
108
3.8.10
PREDICTION:
PLASMA
PHASE
TRANSITION
.
109
3.9
OVERVIEW
FOR
FORMAL
TEMPERATURES
.
110
3.10
DISCUSSION
OF
CHAPTER
3
.
ILL
CONTENTS
11
4
SUPERVISED
&
SELF-ORGANIZED
ADAPTATION
113
4.1
FORMS
OF
SUPERVISED
ADAPTATION
.
113
4.2
OPERANT
CONDITIONING
.
114
4.2.1
THE
PHENOMENON
OF
TRANSITIVE
INFERENCE
.
114
4.2.2
NETWORK
MODEL
.
116
4.2.3
ANALYSIS
OF
THE
NETWORK
MODEL
.
117
4.2.4
TRANSITIVE
INFERENCE
.
119
4.2.5
'
SYMBOLIC
DISTANCE
EFFECT
.
119
4.2.6
NETWORK
PARAMETERS
FOR
VARIOUS
SPECIES
.
121
4.3
GENERALIZED
QUANTITATIVE
DYNAMICAL
ANALYSIS
.
122
4.3.1
GENERAL
VALUATION
DYNAMICS
.
122
4.3.2
TYANSITIVE
INFERENCE
WITH
GENERAL
VALUATION
DYNAMICS
.
123
4.3.3
NECESSARY
AND
SUFFICIENT
CONDITIONS
FOR
LEARNING
THE
PIAGET
TASK
.
123
4.3.4
TYANSITIVE
INFERENCE
AS
A
CONSEQUENCE
OF
SUCCESSFUL
LEARNING
.
124
4.3.5
GENERAL
SET
OF
TASKS
.
125
4.3.6
NETWORK
MODEL
WITH
MINIMIZATION
OF
COMPLEXITY
.
126
4.3.7
COMPLETE
NEUROSYNAPTIC
DYNAMICS
AND
EMPIRICAL
DATA
.
127
4.3.8
DISCUSSION
OF
OPERANT
CONDITIONING
.
130
4.4
SUPERVISED
HEBB-RULE
.
131
4.4.1
NETWORK
MODEL
.
131
4.4.2
NETWORK
ANALYSIS
.
131
4.4.3
DISCUSSION
ON
CONVERGENCE
WITH
HEBB-RULES
.
134
4.5
PERCEPTRON
.
134
4.5.1
NETWORK
AND
TASK
DEFINITION
.
134
4.5.2
NETWORK
ARCHITECTURE
CAPABILITIES
.
135
4.5.3
PERCEPTRON
CONVERGENCE
THEOREM
.
136
4.6
DISCUSSION
OF
CHAPTER
4
.
137
5
ADVANCED
ADAPTATIONS
138
5.1
LEARNING
OF
CHARGES
.
139
5.1.1
AN
ESPECIALLY
SIMPLE
EXPERIMENT
.
140
5.1.2
NECESSARY
INNER
NEURONS
.
141
5.1.3
DEFINITION
OF
FRAMEWORKS
.
141
5.1.4
NETWORK
MODEL
.
142
5.1.5
ANALYSIS
OF
THE
NETWORK
MODEL
.
143
5.1.6
DISCUSSION
.
146
5.2
ATTENTION
.
147
5.2.1
NETWORK
MODEL
WITH
ATTENTION
.
148
5.2.2
POTENTIAL
FIELD
THEOREM
.
148
5.2.3
ATTENTIONAL
LEARNING
OF
CHARGES
.
150
5.2.4
ATTENTIONAL
ADAPTATION
CONVERGENCE
THEOREM
.
151
5.2.5
EMERGENCE
OF
NETWORK
ARCHITECTURES
.
153
5.2.6
GENERALIZED
PERCEPTRON
.
153
5.2.7
NEURONAL
DYNAMICS
WITH
SIGNUM
FUNCTION
.
155
5.2.8
DISCUSSION
.
156
5.3
REVERSAL
.
156
5.3.1
A
REVERSAL
EXPERIMENT
.
157
5.3.2
NETWORK
MODEL
.
157
5.3.3
DISCUSSION
OF
REVERSAL
.
159
12
CONTENTS
5.4
LEARNING
OF
COUNTING
.
159
5.4.1
GENERALIZATION
WITHOUT
LIMITATION
.
159
5.4.2
NETWORK
ARCHITECTURE
AND
DYNAMICS
.
160
5.4.3
ANALYSIS
OF
THE
NETWORK
.
161
5.4.4
AN
INSTRUCTIVE
NETWORK
MODEL
.
162
5.4.5
ADVANCED
NETWORK
DYNAMICS
.
164
5.4.6
ANALYSIS
OF
THE
ADVANCED
NETWORK
MODEL
.
165
5.4.7
A
SOLUTION
OF
WITTGENSTEIN
'
S
PARADOX
.
166
5.4.8
DISCUSSION
.
168
5.5
CONVERGENCE
THEOREM
FOR
INNER
FEEDBACK
.
168
5.5.1
IDEA
OF
ADAPTATION
VIA
SHORT
DIMENSION
INCREASE
.
169
5.5.2
SPECIFICATION
OF
THE
LEARNING
SITUATION
.
170
5.5.3
LEARNING
ALGORITHM
FOR
INNER
FEEDBACK
.
171
5.5.4
CONVERGENCE
THEOREM
.
174
5.5.5
OPTIMAL
CORRESPONDENCE
VIA
SHORT
DIMENSION
INCREASE
.
177
5.5.6
GENERALIZATIONS
.
178
5.5.7
DISCUSSION
.
179
5.6
CORRESPONDENCE
DEFICIT
COMPENSATION:
SCHIZOPHRENIA
MODEL?
.
180
5.6.1
STARTING
POINT
.
180
5.6.2
NETWORK
MODEL
.
181
5.6.3
NETWORK
CHARACTERISTICS
.
182
5.6.4
TRANSFER
TO
SCHIZOPHRENIA
.
185
5.6.5
THERAPY
.
187
5.6.6
EMPIRICAL
FINDINGS
.
188
5.6.7
DISCUSSION
.
194
5.7
A
MESOSCOPIC
PERCEPTION
MODEL
.
195
5.7.1
EXTERNAL
STIMULATIONS
.
195
5.7.2
NETWORK
MODEL
.
197
5.7.3
FIELD
THEORETIC
SOLUTION
OF
THE
NETWORK
.
201
5.7.4
MODELING
PHENOMENA
.
203
5.7.5
DISCUSSION
.
210
5.8
EMERGENT
VALUATION
.
211
5.8.1
EMERGENCE
OF
A
VALUATING
FIELD
.
211
5.8.2
EFFECT
OF
A
VALUATING
STIMULATION
H
T
.
213
5.9
GENERAL
ADAPTATION
DYNAMICS
.
214
5.9.1
DEFINITION
OF
MICROSCOPIC
DYNAMICS
.
214
5.9.2
RESULTING
MACROSCOPIC
DYNAMICS
.
216
5.9.3
SOME
SPECIAL
CASES
.
218
5.10
DISCUSSION
OF
CHAPTER
5
.
219
5.11
NO
LAPLACE
DEMON
.
220
6
SUMMARY
221
6.1
OVERVIEW
.
221
6.2
PREDICTIONS
.
222
6.3
LIST
OF
IDEAS
.
224
6.4
OPEN
QUESTIONS
.
225 |
| any_adam_object | 1 |
| author | Carmesin, Hans-Otto |
| author_GND | (DE-588)1211419223 |
| author_facet | Carmesin, Hans-Otto |
| author_role | aut |
| author_sort | Carmesin, Hans-Otto |
| author_variant | h o c hoc |
| building | Verbundindex |
| bvnumber | BV010716708 |
| classification_rvk | ST 152 |
| ctrlnum | (OCoLC)231662953 (DE-599)BVBBV010716708 |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV010716708 |
| illustrated | Illustrated |
| indexdate | 2024-08-14T00:27:17Z |
| institution | BVB |
| isbn | 3631300395 0820431729 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-007156204 |
| oclc_num | 231662953 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-12 DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-12 DE-188 |
| physical | 236 S. Ill., graph. Darst. |
| publishDate | 1996 |
| publishDateSearch | 1996 |
| publishDateSort | 1996 |
| publisher | Lang |
| record_format | marc |
| spelling | Carmesin, Hans-Otto Verfasser (DE-588)1211419223 aut Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems Hans-Otto Carmesin Frankfurt am Main [u.a.] Lang 1996 236 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Literaturverz. S. 229 - 236 Nervennetz (DE-588)4041638-0 gnd rswk-swf Nervensystem (DE-588)4041643-4 gnd rswk-swf Modell (DE-588)4039798-1 gnd rswk-swf Plastizität Physiologie (DE-588)4174847-5 gnd rswk-swf Anpassung (DE-588)4128128-7 gnd rswk-swf Zentralnervensystem (DE-588)4067637-7 gnd rswk-swf Nervensystem (DE-588)4041643-4 s Anpassung (DE-588)4128128-7 s Nervennetz (DE-588)4041638-0 s Modell (DE-588)4039798-1 s DE-604 Zentralnervensystem (DE-588)4067637-7 s Plastizität Physiologie (DE-588)4174847-5 s DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007156204&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Carmesin, Hans-Otto Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems Nervennetz (DE-588)4041638-0 gnd Nervensystem (DE-588)4041643-4 gnd Modell (DE-588)4039798-1 gnd Plastizität Physiologie (DE-588)4174847-5 gnd Anpassung (DE-588)4128128-7 gnd Zentralnervensystem (DE-588)4067637-7 gnd |
| subject_GND | (DE-588)4041638-0 (DE-588)4041643-4 (DE-588)4039798-1 (DE-588)4174847-5 (DE-588)4128128-7 (DE-588)4067637-7 |
| title | Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems |
| title_auth | Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems |
| title_exact_search | Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems |
| title_full | Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems Hans-Otto Carmesin |
| title_fullStr | Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems Hans-Otto Carmesin |
| title_full_unstemmed | Neuronal adaptation theory including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems Hans-Otto Carmesin |
| title_short | Neuronal adaptation theory |
| title_sort | neuronal adaptation theory including 29 exercises with solutions 43 essential ideas and 108 partially coloured figures experiment explanations and general theorems |
| title_sub | including 29 exercises with solutions, 43 essential ideas and 108 partially coloured figures, experiment explanations, and general theorems |
| topic | Nervennetz (DE-588)4041638-0 gnd Nervensystem (DE-588)4041643-4 gnd Modell (DE-588)4039798-1 gnd Plastizität Physiologie (DE-588)4174847-5 gnd Anpassung (DE-588)4128128-7 gnd Zentralnervensystem (DE-588)4067637-7 gnd |
| topic_facet | Nervennetz Nervensystem Modell Plastizität Physiologie Anpassung Zentralnervensystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=007156204&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT carmesinhansotto neuronaladaptationtheoryincluding29exerciseswithsolutions43essentialideasand108partiallycolouredfiguresexperimentexplanationsandgeneraltheorems |