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.
Neurobiological Models: Integrate and Fire Model; Calculations of Spiking Times; Stochastic Inputs, Poisson Process and The Integrate and Fire Model; Nonlinear Deterministic Models, Stability and Repetitive Firing; Clustering: Cluster Algorithms and Neural Algorithms; Cluster Algorithms and Extreme Events Analysis for Protein Classification; Appendices: Tutorial of Elementary Calculus; Numerical Solutions of I&F Model with Matlab Programs; Simulation of Random Variables and Poisson Processes; Numerical Solution of I&F Model with Random Inputs; Simulation of Non-Linear Neurobiological Models Like Fitzhugh--Nagumo, Hodgkin- Huxley Models; Microarrays; Kohonen Algorithm; Use of Data Base for Proteins and Neural Networks of the Type of Back-Propagation.