Invariants of behavior: constancy and variability in neural systems
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York [u.a.]
Springer
2011
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| Schriftenreihe: | Springer series in cognitive and neural systems
[2] |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | In Vorlage fälschlich "Volume 1" |
| Beschreibung: | XIX, 251 S. Ill., graph. Darst. 24 cm |
| ISBN: | 9781441988034 1441988033 |
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Datensatz im Suchindex
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| adam_text | Titel: Invariants of behavior
Autor: Negrello, Mario
Jahr: 2011
Parti Invariants of Behavior
1 Introduction................................................................... 3
1.1 Variation and Constancy in Neurobiology and Behavior............. 3
1.2 Argument Outline...................................................... 5
1.2.1 Part I: Invariants and Invariants of Behavior................ 5
1.2.2 Models and Dynamical Systems............................. 6
1.2.3 Part II: The Organism and Its World......................... 6
1.2.4 Convergent Evolution of Behavioral Traits.................. 7
1.2.5 Modular Organization........................................ 7
1.2.6 Concept Duals................................................ 8
References...................................................................... 9
2 Invariances in Theory........................................................ 11
2.1 Invariants in Physics and Mathematics................................ 11
2.1.1 Invariants Are That Which Remains When
Something Else Changes..................................... 11
2.1.2 Theoretical Invariants: Speed of Light,
Planck s Constant............................................. 11
2.1.3 Theoretical and Relational Invariants: Energy.............. 12
2.1.4 Empirical and Relational Invariants: Law of Gases......... 12
2.1.5 Context-Dependent Invariances: Sum of
Internal Angles of the Triangle.............................. 13
2.1.6 Invariants Prefigure Theories................................. 14
2.2 Invariants in Biology................................................... 14
2.2.1 Biology Borrowed Invariants from Physics
and Mathematics.............................................. 14
2.2.2 Invariants in Biology Are Context-Dependent.............. 14
2.2.3 Three Examples: Life, Form, and Behavior................. 15
2.2.4 Ontogeny Needs the Environment........................... 17
2.2.5 Development and Biophysics................................ 17
2.2.6 Evolution Operates on Organisms, Not on Genes........... 18
2.2.7 Genes as Invariants of Species Identity...................... 19
Contents
2.2.8 Invariants in Biology: Only Within Narrow
Contextual Boundaries....................................... 19
2.2.9 Genetic Triggers Do Not Build an Organism............... 19
2.3 Invariants of Behavior.................................................. 20
2.3-.1 What Is an Invariant of Behavior?........................... 20
2.3.2 Genes as Invariants of Behavior............................. 20
2.3.3 Genes Are Untenable as Invariants
of Behavior, for They Are a Diluted Cause ................. 20
2.3.4 Neuroanatomy: Invariant Connections
Between Architecture and Behavior......................... 21
2.3.5 Architectonic Invariances.................................... 22
2.3.6 Some Features of Gross Anatomy Can Be
Traced to Behaviors.......................................... 24
2.3.7 Connections Between Anatomy and Function
Do Not Always Exist......................................... 25
2.3.8 Neuroanatomical Variation and Constancy of Function - 25
2.3.9 One to Very Many Mappings from Anatomy
to Function.................................................... 26
2.3.10 Requirements for an Invariant of Behavior.................. 26
2.3.11 Cybernetics, Reafference, and Sensorimotor Loops........ 28
2.3.12 Schema Theory and Functional Overlays................... 31
2.3.13 Behavioral Function and Invariants of Behavior............ 34
2.4 Conclusion.............................................................. 36
2.5 Summary................................................................ 37
References...................................................................... 38
Empirical Assessments of Invariance...................................... 41
3.1 Empirical Assessments of Invariance................................. 41
3.1.1 Invariants in Neuroscience................................... 41
3.1.2 A Brief Typology of Function and Invariance
in Neuroscience............................................... 42
3.2 The Measurement of Neural Activity ................................. 44
3.2.1 Whatever Is Seen, Is Seen Through Lenses................. 44
3.2.2 Contentions on the Measurement of Brain
and Behavioral Function..................................... 44
3.2.3 Scope of Measurement Tools and Analysis Methods....... 45
3.2.4 Oscillations and Potentials: EEG............................ 45
3.2.5 Function Localization: fMRI................................. 48
3.2.6 Brain Wiring: DTI and Diffusion Spectrum Imaging....... 51
3.2.7 Electrophysiology: Single Unit Recordings................. 53
3.2.8 Empirical Methods and the Language of Explanation...... 55
3.3 Sources of Variation.................................................... 55
3.3.1 Instrumental Sources of Variation........................... 55
3.3.2 Repeatability and Variation in Different Levels............. 57
3.3.3 Sources of Variation.......................................... 58
Contents xi
3.4 Conclusions............................................................. 58
3.4.1 Partial Pictures................................................ 58
3.4.2 More Epistemological Contentions.......................... 59
3.5 Summary................................................................ 60
References...................................................................... 60
4 Modeling and Invariance.................................................... 63
4.1 Invariance and Computational Models................................ 63
4.1.1 Shared Invariant Rules from Natural Patterns............... 63
4.1.2 Dynamical Neural Patterns................................... 66
4.2 Computational Models of Neural Invariance......................... 68
4.2.1 Constancy and Variability in Modeling...................... 68
4.2.2 Models and Invariance in Computational Neuroscience.... 69
4.2.3 Hebbian Plasticity............................................ 69
4.2.4 Kohonen s Self-Organizing Maps ........................... 70
4.2.5 Backpropagation: Gradient Descent Methods............... 72
4.2.6 Hopfield Networks............................................ 73
4.2.7 Decorrelation Principles...................................... 74
4.3 Conclusion.............................................................. 77
4.3.1 Invariants and the Structure of the World.................... 77
4.3.2 Constancy and Variability in Modeling...................... 78
References...................................................................... 79
5 Dynamical Systems and Convergence...................................... 81
5.1 Introduction............................................................. 81
5.1.1 Previous Conclusions......................................... 81
5.1.2 Dynamical Systems Theory as an Integrative
Framework.................................................... 82
5.1.3 Outline........................................................ 83
5.2 Dynamical Systems Vocabulary....................................... 84
5.2.1 Dynamical Systems Basic Terminology..................... 84
5.2.2 Coupled Dynamical Systems and Interfaces................ 88
5.3 A Motor Act, as Coupled Dynamical Systems........................ 89
5.3.1 Integrating Levels............................................. 92
5.4 Convergence in the Neuromuscular Junction......................... 93
5.4.1 Convergent Level Crossing................................... 93
5.5 Conclusion.............................................................. 97
5.5.1 Convergence and Level Crossing............................ 97
5.6 Summary................................................................ 98
References...................................................................... 98
6 Neurons, Models, and Invariants...........................................101
6.1 Neurons to Models.....................................................101
6.1.1 The History of the Models of the Action Potential .........102
xii Contents
6.1.2 The Hodgkin-Huxley Model Illustrates How
Variability Converges to Constancy .........................106
6.1.3 Categories Emerge............................................108
6.2 Network Models........................................................Ill
6.2.1 Difficulties with the Hodgkin-Huxley Model...............Ill
6.2.2 A General Template To Build a Network Model............112
6.2.3 Parameterizing Structure to Analyze Dynamics............113
6.2.4 Properties of Units and Properties of the System...........113
6.3 Recurrent Neural Network Models....................................114
6.3.1 Assumptions..................................................114
6.3.2 Discussion of Assumptions..................................115
6.3.3 Fair Assumptions.............................................117
6.4 Conclusion..............................................................117
6.4.1 The Staggering Complexity of the Neuron..................117
6.4.2 Neurons, Networks, and Organismic Behavior.............118
6.5 Addendum: Level Crossing in Models................................119
References......................................................................120
Part II Neurodynamics of Embodied Behavior
7 Neurodynamics and Evolutionary Robotics...............................125
7.1 Crash Course on the Neurodynamics of Recurrent
Neural Networks.......................................................125
7.1.1 Varieties of Neurodynamics..................................125
7.1.2 Definitions....................................................127
7.1.3 Neurodynamics and Attractors...............................135
7.2 Evolutionary Robotics at a Glance....................................136
7.2.1 Neurodynamics and Evolutionary Robotics.................136
7.2.2 From Evolution of Organisms to Evolutionary
Algorithms....................................................136
7.2.3 Assumptions of an Evolutionary Robotics Problem........137
7.2.4 Structural Evolution and Simulation.........................138
7.3 Conclusion..............................................................139
References......................................................................139
8 Attractor Landscapes and the Invariants of Behavior...................141
8.1 Introduction.............................................................141
8.1.1 Outlook........................................................141
8.1.2 Definitions....................................................145
8.2 Toy Problem in Active Tracking.......................................147
8.3 Methods.................................................................148
8.3.1 Problem Description..........................................148
8.3.2 Challenges for the Tracker...................................150
8.3.3 Convergence and Motor Projections of Attractors..........151
8.4 Results..................................................................152
8.4.1 Tracking Behavior Across Attractors........................152
Contents xiii
8.4.2 Solutions......................................................152
8.4.3 Analysis of Dynamical Entities Generating Behavior......153
8.4.4 Convergent Activity and Equivalence of Attractors.........153
8.4.5 Features of Evolved Attractor Landscapes..................154
8.4.6 Attractor Shapes and Action.................................159
8.5 Discussion..............................................................163
8.5.1 Structure-Attractor Landscape-Function...................163
8.5.2 Invariants of Behavior........................................163
8.5.3 Explanations of Functional Behavior:
Negative Feedback............................................166
8.5.4 Behavioral Function Demands a Holistic
Description ...................................................169
8.6 Linking Section: Convergent Landscapes.............................170
8.6.1 Direct Association Between Dynamics and Behavior......170
Appendix I: Learning as Deforming Attractor Landscapes..................172
Appendix II: Related Work....................................................173
References......................................................................174
9 Convergent Evolution of Behavioral Function............................177
9.1 Convergent Evolution..................................................177
9.1.1 Outlook........................................................177
9.2 Preliminaries............................................................178
9.2.1 Is Evolution Gradual or Punctuated?........................178
9.2.2 The Moment of Invention of Function.......................179
9.2.3 Neutral Mutation and Appearance of Function.............180
9.2.4 Convergent Evolution Controversy..........................182
9.3 Evolutionary Phenomena in Evolution of Tracking and
of Following............................................................186
9.3.1 Invariance Organized Through Orderings and Selection ... 186
9.3.2 Gradual Improvement in Evolution of Simple Tracking.... 187
9.3.3 Extensions to the Experiment on the Evolution
of Tracking....................................................192
9.4 Discussion: Convergent Evolution and Instinct.......................201
9.4.1 Convergent Evolution of Attractor Landscapes.............201
9.4.2 Instinct: Convergent Evolution of Functional Behavior .... 202
9.5 Constancy and Variability in Structure and Function.................203
9.5.1 Aspects of Constancy .......................................204
9.5.2 Sources of Variability........................................206
9.6 Summary................................................................208
9.6.1 Convergent Function and Invariants of Behavior...........208
9.7 Linking Section: Evolution and Modularity..........................209
9.7.1 Functional Selection of Modular Structures
and Dynamics.................................................210
References......................................................................211
xiv Contents
10 Neural Communication: Messages Between Modules....................213
10.1 Introduction.............................................................213
10.1.1 Neural Modularization.......................................214
10.1.2 The Meaning of Neurons, Modules,
and Attractor Landscapes....................................215
10.1.3 Outline........................................................216
10.2 Modularity..............................................................216
10.2.1 Functional Semiotics of Neurons and Networks............218
10.2.2 Anatomical Modularity.......................................219
10.2.3 Heuristics for Modularity....................................220
10.3 Types of Modularity....................................................221
10.3.1 Vertical Modularity...........................................222
10.3.2 Monolithic Modules and Dynamic Modularity.............229
10.4 Equivalence, Variability, and Noise ...................................230
10.4.1 The Geometry of Dynamical Modularity....................231
10.4.2 Other Mechanisms of Dynamical Modularity...............231
10.5 Discussion: Modular Function and Attractor Landscapes............234
10.5.1 Modules, Attractor Landscapes, and Meaning..............234
10.6 Summary................................................................235
References......................................................................236
11 Conclusion.....................................................................239
11.1 The Search for Mechanism Within a Meaningful World.............239
11.1.1 Contraptions, Analogies, and Explanation..................239
11.1.2 Empirical Invariances: Flickering Lights....................240
11.2 The Current Stage......................................................243
Reference.......................................................................244
Index.................................................................................245
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| id | DE-604.BV039645521 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:08:09Z |
| institution | BVB |
| isbn | 9781441988034 1441988033 |
| language | English |
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| physical | XIX, 251 S. Ill., graph. Darst. 24 cm |
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| series2 | Springer series in cognitive and neural systems |
| spelling | Negrello, Mario Verfasser aut Invariants of behavior constancy and variability in neural systems Mario Negrello New York [u.a.] Springer 2011 XIX, 251 S. Ill., graph. Darst. 24 cm txt rdacontent n rdamedia nc rdacarrier Springer series in cognitive and neural systems [2] In Vorlage fälschlich "Volume 1" Nervous System Physiological Phenomena Models, Neurological Behavior physiology Nerve Net physiology Neurophysiologie (DE-588)4041897-2 gnd rswk-swf Variabilität (DE-588)4264568-2 gnd rswk-swf Verhalten (DE-588)4062860-7 gnd rswk-swf Nervensystem (DE-588)4041643-4 gnd rswk-swf Nervensystem (DE-588)4041643-4 s Verhalten (DE-588)4062860-7 s Variabilität (DE-588)4264568-2 s DE-604 Neurophysiologie (DE-588)4041897-2 s b DE-604 Springer series in cognitive and neural systems [2] (DE-604)BV039686781 2 HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024495342&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Negrello, Mario Invariants of behavior constancy and variability in neural systems Springer series in cognitive and neural systems Nervous System Physiological Phenomena Models, Neurological Behavior physiology Nerve Net physiology Neurophysiologie (DE-588)4041897-2 gnd Variabilität (DE-588)4264568-2 gnd Verhalten (DE-588)4062860-7 gnd Nervensystem (DE-588)4041643-4 gnd |
| subject_GND | (DE-588)4041897-2 (DE-588)4264568-2 (DE-588)4062860-7 (DE-588)4041643-4 |
| title | Invariants of behavior constancy and variability in neural systems |
| title_auth | Invariants of behavior constancy and variability in neural systems |
| title_exact_search | Invariants of behavior constancy and variability in neural systems |
| title_full | Invariants of behavior constancy and variability in neural systems Mario Negrello |
| title_fullStr | Invariants of behavior constancy and variability in neural systems Mario Negrello |
| title_full_unstemmed | Invariants of behavior constancy and variability in neural systems Mario Negrello |
| title_short | Invariants of behavior |
| title_sort | invariants of behavior constancy and variability in neural systems |
| title_sub | constancy and variability in neural systems |
| topic | Nervous System Physiological Phenomena Models, Neurological Behavior physiology Nerve Net physiology Neurophysiologie (DE-588)4041897-2 gnd Variabilität (DE-588)4264568-2 gnd Verhalten (DE-588)4062860-7 gnd Nervensystem (DE-588)4041643-4 gnd |
| topic_facet | Nervous System Physiological Phenomena Models, Neurological Behavior physiology Nerve Net physiology Neurophysiologie Variabilität Verhalten Nervensystem |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=024495342&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV039686781 |
| work_keys_str_mv | AT negrellomario invariantsofbehaviorconstancyandvariabilityinneuralsystems |