Computational Systems NeurobiologyN. Le Novère Springer Science & Business Media, 20.07.2012 - 572 Seiten Computational neurosciences and systems biology are among the main domains of life science research where mathematical modeling made a difference. This book introduces the many different types of computational studies one can develop to study neuronal systems. It is aimed at undergraduate students starting their research in computational neurobiology or more senior researchers who would like, or need, to move towards computational approaches. Based on their specific project, the readers would then move to one of the more specialized excellent textbooks available in the field. The first part of the book deals with molecular systems biology. Functional genomics is introduced through examples of transcriptomics and proteomics studies of neurobiological interest. Quantitative modelling of biochemical systems is presented in homogeneous compartments and using spatial descriptions. A second part deals with the various approaches to model single neuron physiology, and naturally moves to neuronal networks. A division is focused on the development of neurons and neuronal systems and the book closes on a series of methodological chapters. From the molecules to the organ, thinking at the level of systems is transforming biology and its impact on society. This book will help the reader to hop on the train directly in the tank engine. |
Inhalt
| 1 | |
| 23 | |
Chapter 3 Using Chemical Kinetics to Model Neuronal Signalling Pathways | 81 |
Chapter 4 Breakdown of MassAction Laws in Biochemical Computation | 119 |
Chapter 5 Spatial Organization and Diffusion in Neuronal Signaling | 133 |
Chapter 6 The Performance and Limits of Simple Neuron Models Generalizations of the Leaky IntegrateandFire Model | 162 |
Chapter 7 Multicompartmental Models of Neurons | 193 |
Chapter 8 Noise in Neurons and Other Constraints | 227 |
Chapter 11 Cooperative Populations of Neurons Mean Field Models of Mesoscopic Brain Activity | 316 |
Chapter 12 Cellular Spacing Analysis and Modelling of Retinal Mosaics | 365 |
Chapter 13 Measuring and Modeling Morphology How Dendrites Take Shape | 387 |
Chapter 14 Axonal Growth and Targeting | 428 |
Chapter 15 Encoding Neuronal Models in SBML | 459 |
Chapter 16 NeuroML | 489 |
Chapter 17 XPPAUT | 519 |
Chapter 18 NEST by Example An Introduction to the Neural Simulation Tool NEST | 532 |
Chapter 9 Methodological Issues in Modelling at Multiple Levelsof Description | 258 |
Chapter 10 Virtues Pitfalls and Methodology of Neuronal Network Modeling and Simulations on Supercomputers | 283 |
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action potential activity algorithm analysis annotation apply axons basal ganglia behaviour bifurcation binding biochemical Bioinformatics Biol biological brain branches calcium cell types cellular channel noise compartment complex components computational neuroscience concentration connectivity constant cortex cortical database defined dendritic dendritic spines density depends described diameter differential diffusion distribution dynamics effects equations example excitatory experimental Faisal function gene expression geometry gradient growth cone Hodgkin-Huxley inhibitory initial input integration ion channels kinetics layer ligand Liley mean field mechanisms membrane potential methods microtubules modulation molecular molecules morphology mosaic multiple NEST network models neural networks neurite NeuroML neuron models nodes parameters pathways PIP2 population postsynaptic propagation properties proteomics Purkinje Purkinje cell pyramidal cells random reaction receptor retinal SBML signal simulation soma spatial specific spike spine neck stochastic structure studies synaptic synaptic plasticity Systems Biology target threshold variability voltage XPPAUT