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.

Contents:

• Neurobiological Models:
• RC Circuit, Spiking Times and Interspike Interval
• Calculation of Interspike Intervals for Deterministic Inputs
• The Fitzhugh-Nagumo and Hodgkin-Huxley Models
• Definition and Simulation of the Main Random Variables
• Simulation of the Neuron Dynamics in Interaction with a Complex Network
• Clustering:
• An Introduction to Clustering Techniques and Self-Organizing Algorithms
• Clustering and Classification Algorithms Applied to Protein Sequences, Structures and Functions
• Appendices:
• Tutorial of Elementary Calculus
• Complements to Chapter 2
• Complements to Chapter 3
• Complements to Chapter 4
• Complements to Chapter 5
• Microarrays
• Complements to Chapter 6
• Mathematical Description of Kohonen Algorithms