Benuskova / Kasabov Computational Neurogenetic Modeling

2007
ISBN: 978-0-387-48355-9
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 290 Seiten

Reihe: Topics in Biomedical Engineering. International Book Series

Computational Neurogenetic Modeling 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, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.

Autoren/Hrsg.

Benuskova, Lubica

Kasabov, Nikola K.

Inhaltsverzeichnis

1;Dedication;6
2;Preface;7
3;Table of Contents;9
4;1 Computational Neurogenetic Modeling (CNGM): A Brief Introduction;13
4.1;1.1 Motivation - The Evolving Brain;13
4.2;1.2 Computational Models of the Brain;16
4.3;1.3 Brain-Gene Data, Information and Knowledge;18
4.4;1.4 CNGM: How to Integrate Neuronal and Gene Dynamics?;24
4.5;1.5 What Computational Methods to Use for CNGM?;26
4.6;1.6 About the Book;27
4.7;1.7 Summary;28
5;2 Organization and Functions of the Brain;29
5.1;2.1 Methods of Brain Study;30
5.2;2.2 Overall Organization of the Brain and Motor Control;35
5.3;2.3 Learning and Memory;37
5.4;2.4 Language and Other Cognitive Functions;41
5.4.1;2.4.1 Innate or Learned?;41
5.4.2;2.4.2 Neural Basis of Language;42
5.4.3;2.4.3 Evolution of Language, Thinking and the Language Gene;45
5.5;2.5 Neural Representation of Information;48
5.6;2.6 Perception;49
5.7;2.7 Consciousness;53
5.7.1;2.7.1 Neural Correlates of Sensory Awareness;53
5.7.2;2.7.2 Neural Correlates of Reflective Consciousness;56
5.8;2.8 Summary and Discussion;61
6;3 Neuro-Information Processing in the Brain;65
6.1;3.1 Generation and Transmission of Signals by Neurons;65
6.2;3.2 Learning Takes Place in Synapses: Toward the Smartness Gene;68
6.3;3.3 The Role of Spines in Learning;70
6.4;3.4 Neocortical Plasticity;73
6.4.1;3.4.1 Developmental Cortical Plasticity;73
6.4.2;3.4.2 Adult Cortical Plasticity;76
6.4.3;3.4.3 Insights into Cortical Plasticity via a Computational Model;78
6.5;3.5 Neural Coding: the Brain is Fast, Neurons are Slow;86
6.5.1;3.5.1 Ultra-Fast Visual Classification;86
6.5.2;3.5.2 Hypotheses About a Neural Code;89
6.5.2.1;Coding Based on Spike Timing;89
6.5.2.2;The Rate Code;89
6.6;3.6 Summary;90
7;4 Artificial Neural Networks (ANN);93
7.1;4.1 General Principles;93
7.2;4.2 Models of Learning in Connectionist Systems;96
7.3;4.3 Unsupervised Learning (Self Organizing Maps - SOM);105
7.3.1;4.3.1 The SOM Algorithm;105
7.3.2;4.3.2 SOM Output;107
7.3.2.1;Sample Distribution;107
7.3.2.2;Clustering Information;107
7.3.2.3;Visualization of Input Variables;108
7.3.2.4;Relationship Between Multiple Descriptors;108
7.3.2.5;The Connection Weights;108
7.3.2.6;Interpretation by the Fuzzy Set Theory;109
7.3.3;4.3.3 SOM for Brain and Gene Data Clustering;109
7.4;4.4 Supervised Learning;110
7.4.1;4.4.1 Multilayer Perceptron (MLP);110
7.4.2;4.4.2 MLP for Brain and Gene Data Classification;111
7.4.2.1;Example;111
7.5;4.5 Spiking Neural Networks (SNN);114
7.6;4.6 Summary;117
8;5 Evolving Connectionist Systems (ECOS);119
8.1;5.1 Local Learning in ECOS;119
8.2;5.2 Evolving Fuzzy Neural Networks EFuNN;120
8.3;5.3 The Basic EFuNN Algorithm;124
8.4;5.4 DENFIS;128
8.4.1;5.4.1 Dynamic Takagi-Sugeno Fuzzy Inference Engine;140
8.4.2;5.4.2 Fuzzy Rule Set, Rule Insertion and Rule Extraction;141
8.5;5.5 Transductive Reasoning for Personalized Modeling;142
8.5.1;5.5.1 Weighted Data Normalization;144
8.6;5.6 ECOS for Brain and Gene Data Modeling;144
8.6.1;5.6.1 ECOS for EEG Data Modeling, Classification and Signal Transition Rule Extraction;144
8.6.2;5.6.2 ECOS for Gene Expression Profiling;146
8.7;5.7 Summary;148
9;6 Evolutionary Computation for Model and Feature Optimization;149
9.1;6.1 Lifelong Learning and Evolution in Biological Species: Nurture vs. Nature;149
9.2;6.2 Principles of Evolutionary Computation;150
9.3;6.3 Genetic Algorithms;150
9.4;6.4 EC for Model and Parameter Optimization;155
9.4.1;6.4.1 Example;155
9.5;6.5 Summary;158
10;7 Gene/Protein Interactions - Modeling Gene Regulatory Networks (GRN) ;159
10.1;7.1 The Central Dogma of Molecular Biology;159
10.2;7.2 Gene and Protein Expression Data Analysis and Modeling;163
10.2.1;7.2.1 Example;165
10.3;7.3 Modeling Gene/Protein Regulatory Networks (GPRN);167
10.4;7.4 Evolving Connectionist Systems (ECOS) for GRN Modeling;172
10.4.1;7.4.1 General Principles;172
10.4.2;7.4.2 A Case Study on a Small GRN Modeling with the Use of ECOS;173
10.5;7.5 Summary;175
11;8 CNGM as Integration of GPRN, ANN and Evolving Processes;177
11.1;8.1 Modeling Genetic Control of Neural Development;178
11.2;8.2 Abstract Computational Neurogenetic Model;183
11.3;8.3 Continuous Model of Gene-Protein Dynamics;187
11.4;8.4 Towards the Integration of CNGM and Bioinformatics;193
11.5;8.5 Summary;197
12;9 Application of CNGM to Learning and Memory;199
12.1;9.1 Rules of Synaptic Plasticity and Metaplasticity;199
12.2;9.2 Toward a GPRN of Synaptic Plasticity;207
12.3;9.3 Putative Molecular Mechanisms of Metaplasticity;215
12.4;9.4 A Simple One Protein-One Neuronal Function CNGM;218
12.5;9.5 Application to Modeling of L-LTP;220
12.6;9.6 Summary and Discussion;224
13;10 Applications of CNGM and Future Development;226
13.1;10.1 CNGM of Epilepsy;227
13.1.1;10.1.1 Genetically Caused Epilepsies;227
13.1.2;10.1.2 Discussion and Future Developments;230
13.2;10.2 CNGM of Schizophrenia;231
13.2.1;10.2.1 Neurotransmitter Systems Affected in Schizophrenia;233
13.2.2;10.2.2 Gene Mutations in Schizophrenia;235
13.2.3;10.2.3 Discussion and Future Developments;238
13.3;10.3 CNGM of Mental Retardation;239
13.3.1;10.3.1 Genetic Causes of Mental Retardation;240
13.3.2;10.3.2 Discussion and Future Developments;244
13.4;10.4 CNGM of Brain Aging and Alzheimer Disease;245
13.5;10.5 CNGM of Parkinson Disease;250
13.6;10.6 Brain-Gene Ontology;253
13.7;10.7 Summary;256
14;Appendix 1;258
14.1;A.1 Table of Genes and Related Brain Functions and Diseases;258
15;Appendix 2;268
15.1;A.2 A Brief Overview of Computational Intelligence Methods;268
15.1.1;A.2.1 Probabilistic and Statistical Methods;268
15.1.2;A.2.2 Boolean and Fuzzy Logic Models;271
15.1.3;A.2.3 Artificial Neural Networks;274
15.1.4;A.2.4 Methods of Evolutionary Computation (EC);277
16;Appendix 3;278
16.1;A.3 Some Sources of Brain-Gene Data, Information, Knowledge and Computational Models;278
17;References;280
18;Index;308

Leseproben

" (p. 56-57)

For major discoveries in the field of synaptic mechanisms of learning, the 2000 Nobel Prize for medicine went to the neuroscientists Eric R. Kandel and Paul Greengard. The 3rd laureate, Arvid Carlsson, got his share of the prize for discoveries of actions of neurotransmitter dopamine. At present, it is widely accepted that learning is accompanied by changes of synaptic weights in cortical neural networks (Kandel et al. 2000). Changes of synaptic weights are also called synaptic plasticity. In 1949, the Canadian psychologist Donald Hebb formulated a universal rule for these changes: "When an axon of cell A excites cell B and repeatedly or persistently takes part in firing it, some growth process or metabolic change takes place in one or both cells so that As efficiency as one of the cells firing B is increased", which has been verified in many experiments and its mechanisms elucidated (Hebb 1949).

In cerebral cortex and in hippocampus of humans and animals, learning takes place in excitatory synapses formed upon dendritic spines that use glutamate as their neurotransmitter. In the regime of learning, glutamate acts on specific postsynaptic receptors, the so-called NMDA receptors (Nmethyl- D-aspartate). NMDA receptors are associated with ion channels for sodium and calcium (see Fig. 3.3). The influx of these ions into spines is proportional to the frequency of incoming presynaptic spikes. Calcium acts as a second messenger thus triggering a cascade of biochemical reactions which lead either to the long-term potentiation of synaptic weights (LTP) or to the long-term depression (weakening) of synaptic weights (LTD).

In experimental animals, it has been recorded that these changes in synaptic weights can last for hours, days, even weeks and months, up to a year. Induction of such long-term synaptic changes involves transient changes in gene expression (Mayford and Kandel 1999, Abraham et al. 2002). A subcellular switch between LTD and LTP is the concentration of calcium within spines (Shouval, Bear et al. 2002). We speak about an LTD/LTP threshold. In tum, the intra-spine calcium concentration depends upon the intensity of synaptic stimulation that is upon the frequency of presynaptic spikes.

That is, more presynaptic spikes mean more glutamate within synaptic cleft. Release of glutamate must coincide with a sufficient depolarization of the postsynaptic membrane to remove the magnesium block ofthe NMDA receptor. The greater the depolarization, the more ions of calcium enters the spine. Postsynaptic depolarization is primarily achieved via AMPA (amino-methylisoxasole-propionic acid) receptors, however, recently a significant role ofbackpropagating postsynaptic spikes has been pointed out (Markram et al. 1997). Calcium concentrations below or above the LTD/LTP threshold, switch on different enzymatic pathways that lead either to LTD or LTP, respectively. However, the current value of the LTD/LTP threshold (i.e. the properties of these two enzymatic pathways) can be influenced by levels of other neurotransmitters, an average previous activity of a neuron, and possibly other biochemical factors as well.

This phenomenon is called metaplasticity, a plasticity of synaptic plasticity (Abraham and Bear 1996). Dependence of the LTD/LTP threshold upon different postsynaptic factors is the subject of the Bienenstock, Cooper and Munro (BCM) theory of synaptic plasticity (Bienenstock et al. 1982) (for a nice overview see for instance (Jedlicka 2002)). The BCM theory of synaptic plasticity has been successfully applied in computer simulations to explain experience-dependent changes in the normal and ultrastructurally altered brain cortex of experimental animals (Benuskova et al. 1994)."