This is a student text, introducing the scope and problems of a new scientific discipline - Computational Neurogenetic Modeling (CNGM). CNGM is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This new area brings together knowledge from various scientific disciplines.
Author(s): Lubica Benuskova, Nikola Kasabov
Edition: 1
Year: 2007
Language: English
Pages: 304
Tags: Биологические дисциплины;Матметоды и моделирование в биологии;
0387483535......Page 1
Computational Neurogenetic
Modeling......Page 4
Copyright Page
......Page 5
Dedication......Page 6
Preface......Page 7
Table of Contents
......Page 9
1.1 Motivation - The Evolving Brain......Page 13
1.2 Computational Models of the Brain......Page 16
1.3 Brain-Gene Data, Information and Knowledge......Page 18
1.4 CNGM: How to Integrate Neuronal and Gene Dynamics?......Page 24
1.5 What Computational Methods to Use for CNGM?......Page 26
1.6 About the Book......Page 27
1.7 Summary......Page 28
2 Organization and Functions of the Brain......Page 29
2.1 Methods of Brain Study......Page 30
2.2 Overall Organization of the Brain and Motor Control......Page 35
2.3 Learning and Memory......Page 37
2.4.1 Innate or Learned?......Page 41
2.4.2 Neural Basis of Language......Page 42
2.4.3 Evolution of Language, Thinking and the Language Gene......Page 45
2.5 Neural Representation of Information......Page 48
2.6 Perception......Page 49
2.7.1 Neural Correlates of Sensory Awareness......Page 53
2.7.2 Neural Correlates of Reflective Consciousness......Page 56
2.8 Summary and Discussion......Page 61
3.1 Generation and Transmission of Signals by Neurons......Page 64
3.2 Learning Takes Place in Synapses: Toward the Smartness Gene......Page 67
3.3 The Role of Spines in Learning......Page 69
3.4.1 Developmental Cortical Plasticity......Page 72
3.4.2 Adult Cortical Plasticity......Page 75
3.4.3 Insights into Cortical Plasticity via a Computational Model......Page 77
3.5.1 Ultra-Fast Visual Classification......Page 85
The Rate Code......Page 88
3.6 Summary......Page 89
4.1 General Principles......Page 92
4.2 Models of Learning in Connectionist Systems......Page 95
4.3.1 The SOM Algorithm......Page 104
Clustering Information......Page 106
The Connection Weights......Page 107
4.3.3 SOM for Brain and Gene Data Clustering......Page 108
4.4.1 Multilayer Perceptron (MLP)......Page 109
Example......Page 110
4.5 Spiking Neural Networks (SNN)......Page 113
4.6 Summary......Page 116
5.1 Local Learning in ECOS......Page 118
5.2 Evolving Fuzzy Neural Networks EFuNN......Page 119
5.3 The Basic EFuNN Algorithm......Page 123
5.4 DENFIS......Page 127
5.4.1 Dynamic Takagi-Sugeno Fuzzy Inference Engine......Page 139
5.4.2 Fuzzy Rule Set, Rule Insertion and Rule Extraction......Page 140
5.5 Transductive Reasoning for Personalized Modeling......Page 141
5.6.1 ECOS for EEG Data Modeling, Classification and Signal Transition Rule Extraction......Page 143
5.6.2 ECOS for Gene Expression Profiling......Page 145
5.7 Summary......Page 147
6.1 Lifelong Learning and Evolution in Biological Species: Nurture vs. Nature......Page 148
6.3 Genetic Algorithms......Page 149
6.4.1 Example......Page 154
6.5 Summary......Page 157
7.1 The Central Dogma of Molecular Biology......Page 158
7.2 Gene and Protein Expression Data Analysis and Modeling......Page 162
7.2.1 Example......Page 164
7.3 Modeling Gene/Protein Regulatory Networks (GPRN)......Page 166
7.4.1 General Principles......Page 171
7.4.2 A Case Study on a Small GRN Modeling with the Use of ECOS......Page 172
7.5 Summary......Page 174
8 CNGM as Integration of GPRN, ANN and Evolving Processes......Page 175
8.1 Modeling Genetic Control of Neural Development......Page 176
8.2 Abstract Computational Neurogenetic Model......Page 181
8.3 Continuous Model of Gene-Protein Dynamics......Page 185
8.4 Towards the Integration of CNGM and Bioinformatics......Page 191
8.5 Summary......Page 195
9.1 Rules of Synaptic Plasticity and Metaplasticity......Page 197
9.2 Toward a GPRN of Synaptic Plasticity......Page 205
9.3 Putative Molecular Mechanisms of Metaplasticity......Page 213
9.4 A Simple One Protein-One Neuronal Function CNGM......Page 216
9.5 Application to Modeling of L-LTP......Page 218
9.6 Summary and Discussion......Page 222
10 Applications of CNGM and Future Development......Page 224
10.1.1 Genetically Caused Epilepsies......Page 225
10.1.2 Discussion and Future Developments......Page 228
10.2 CNGM of Schizophrenia......Page 229
10.2.1 Neurotransmitter Systems Affected in Schizophrenia......Page 231
10.2.2 Gene Mutations in Schizophrenia......Page 233
10.2.3 Discussion and Future Developments......Page 236
10.3 CNGM of Mental Retardation......Page 237
10.3.1 Genetic Causes of Mental Retardation......Page 238
10.3.2 Discussion and Future Developments......Page 242
10.4 CNGM of Brain Aging and Alzheimer Disease......Page 243
10.5 CNGM of Parkinson Disease......Page 248
10.6 Brain-Gene Ontology......Page 251
10.7 Summary......Page 254
A.1 Table of Genes and Related Brain Functions and Diseases
......Page 256
A.2.1 Probabilistic and Statistical Methods......Page 266
A.2.2 Boolean and Fuzzy Logic Models......Page 269
A.2.3 Artificial Neural Networks......Page 272
A.2.4 Methods of Evolutionary Computation (EC)......Page 275
A.3 Some Sources of Brain-Gene Data, Information, Knowledge and Computational Models
......Page 276
References......Page 278
Index......Page 306
