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Introduction To Computational Neurobiology And Clustering [Hardback]

(Univ Of Rome La Sapienza, Italy), (Univ Of Rome La Sapienza, Italy), (Sapienza Univ Of Rome, Italy)
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Introduction To Computational Neurobiology And ClusteringPalielināt
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Permanent link: https://www.kriso.lv/db/9789812705396.html
Keywords:
This volume provides students with the necessary tools to better understand the fields of neurobiological modeling, cluster analysis of proteins and genes. The theory is explained starting from the beginning and in the most elementary terms, there are many exercises solved and not useful for the understanding of the theory. The exercises are specially adapted for training and many useful Matlab programs are included, easily understood and generalizable to more complex situations. This self-contained text is particularly suitable for an undergraduate course of biology and biotechnology. New results are also provided for researchers such as the description and applications of the Kohonen neural networks to gene classification and protein classification with back propagation neutral networks.
Preface vii
Neurobiological models
1(98)
RC circuit, spiking times and interspike interval
3(10)
Introduction
3(1)
Electric properties of a neuron
3(3)
Lapicque or I&; F model
6(7)
Calculation of interspike intervals for deterministic inputs
13(18)
Introduction
13(1)
Case of constant input current
13(4)
Constant input current for a finite time
17(3)
Constant input current with a periodic pattern
20(3)
Periodic instantaneous inputs
23(3)
Exercises
26(5)
The Fitzhugh-Nagumo and Hodgkin-Huxley models
31(24)
Introduction
31(1)
The Fitzhugh-Nagumo model and the general properties of differential equations
32(5)
The generation of spikes and the Hopf bifurcation
37(12)
A more realistic model: the Hodgkin-Huxley model (HH model)
49(6)
Definition and simulation of the main random variables
55(24)
Introduction
55(1)
General definitions
56(4)
Uniformly distributed random variable
60(4)
Exponentially distributed random variables
64(4)
Gaussian random variables
68(4)
Poisson random variables
72(7)
Simulation of the neuron dynamics in interaction with a complex network
79(20)
Introduction
79(2)
Definition of a Poisson process
81(1)
The integrate and fire model with Poissonian inputs
82(3)
Computation of interspike intervals with Poissonian inputs
85(4)
Numeric computation of interspike intervals with Poissonian inputs
89(5)
Neural computation with Brownian inputs
94(5)
Clustering
99(62)
An introduction to clustering techniques and self-organizing algorithms
101(38)
A brief overview of clustering technique
101(1)
Distance metric
102(2)
Clustering algorithms
104(1)
Hierarchical methods
104(2)
Non-hierarchical methods
106(1)
Graph-theoretic clustering
107(2)
Cast
109(1)
The Kohonen network
110(6)
Numerical investigations and applications of Kohonen algorithm
116(13)
Conclusion
129(2)
Comments and comparison with other algorithms
131(8)
Clustering and classification algorithms applied to protein sequences, structures and functions
139(22)
Working with proteins information
139(1)
Protein sequence similarity
140(6)
Protein structure similarity
146(2)
Protein-protein interaction
148(1)
Experimental methods to identify protein ligands
149(3)
Computational methods to characterize protein ligands
152(4)
The neural network approach
156(5)
Appendices
161(58)
Appendix A Tutorial of elementary calculus
163(2)
Derivation of the results of
Chapter 1
163(2)
Appendix B Complements to
Chapter 2
165(12)
Solution of the Exercises of
Chapter 2
165(5)
Matlab programs
170(7)
Appendix C Complements to
Chapter 3
177(1)
Main definitions of matrix calculus
177(1)
Matlab programs for integrating the FN and HH models
178(3)
Appendix D Complements to
Chapter 4
181(16)
A simple introduction to probability
181(7)
Program for simulating the U(0,1) random variables
188(1)
Program for simulating the exponentially distributed r.v.
189(2)
Program for simulating the Gaussian N(0,1) r.v
191(1)
Program for simulating the Poisson random variables
192(5)
Appendix E Complements to
Chapter 5
197(8)
Matlab program for simulating the process of Lemma 5.2
197(2)
Matlab program for simulating the case of two input Poisson processes
199(2)
Matlab program for solving the system (5.22)
201(4)
Appendix F Microarrays
205(6)
Measuring gene expression
205(3)
Applications of microarray
208(3)
Appendix G Complements to
Chapter 6
211(4)
Kohonen algorithm in Matlab source
211(4)
Appendix H Mathematical description of Kohonen algorithms
215(4)
Convergence of Kohonen algorithm
215(4)
Bibliography 219(6)
Subject Index 225(4)
Author Index 229