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The final chapter has descriptions of a number of more advanced or new topics in neuroengineering. Numerical methods are outlined so students with programming knowledge can implement the models presented in the text. The first chapter is a review of the structure of the neuron and anatomy of the brain. Chapters derive the theory of active and passive membranes, electrical propagation in axons and dendrites and the dynamics of the synapse. Chapter 7 is an introduction to modeling networks of neurons and artificial neural networks.

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The final chapter has descriptions of a number of more advanced or new topics in neuroengineering. Numerical methods are outlined so students with programming knowledge can implement the models presented in the text. The first chapter is a review of the structure of the neuron and anatomy of the brain. Chapters derive the theory of active and passive membranes, electrical propagation in axons and dendrites and the dynamics of the synapse. Chapter 7 is an introduction to modeling networks of neurons and artificial neural networks. Ggggb

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  1. Numerical methods are outlined so students with programming knowledge can implement the models presented in the text. The final chapter has descriptions of a number of more advanced or new topics in neuroengineering.

  2. Chapters derive the theory of active and passive membranes, electrical propagation in axons and dendrites and the dynamics of the synapse. A refresher on basic statistics and an introduction to R Considerations and techniques for the visual display of data through graphics An overview of statistical hypothesis tests and the reasoning behind them A comprehensive guide to the use of the linear model, the foundation of most statistics encountered An introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regression Instruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may exist Focusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research.

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