The noun “dynamics” entered the English language in the eighteenth century, when natural philosophers, following the lead of Isaac Newton, began thinking of motion in terms of applied forces and the resulting accelerations. In 1788, the New Royal Encyclopaedia contained the definition, “Dynamics is the science of moving powers; more particularly of the motion of bodies that mutually act on one another.” This is still a useful definition. For the purposes of this book, we can define dynamics as the study of objects that move while interacting through mutual forces.

How did new forms of mass politics and culture that included and gave voice to all men and women finally develop in Britain between 1885 and 1931?

Britain remained the world’s superpower in 1931, so how did it lose its Empire, become dependent upon the USA and reimagine itself as a European nation by 1976 and how did Briton’s respond?

A gravitationally bound two-body system (if the two bodies are spheres of constant mass) shows simple periodic motion. We have seen that a three-body system, even if we install restrictions for computational simplicity, can show a rich variety of behaviors. Tadpole orbits, horseshoe orbits, and ZLK oscillations are just a sampling of what can happen.

This chapter highlights the cognitive neuroscience techniques that have been employed in the past and the techniques that will be employed in the future. Section 12.1 describes the similarities between fMRI and phrenology, a pseudoscience from about two centuries ago in which protrusions of the skull were associated with behavioral characteristics. In Section 12.2, fMRI is directly compared to ERPs. Section 12.3 discusses research investigating brain region interactions. This type of research has only recently started to be conducted and involves brain activity frequency analysis or modulating one brain region and measuring how that changes activity in another brain region. Section 12.4 provides an overview of the field of cognitive neuroscience in the future. The final section shines a spotlight on the dimension of time. To date, temporal processing in the brain has received less attention than spatial localization. However, time is the future of the cognitive neuroscience of memory.

This chapter covers digital information sources in some depth. It provides intuition on the information content of a digital source and introduces the notion of redundancy. As a simple but important example, discrete memoryless sources are described. The concept of entropy is defined as a measure of the information content of a digital information source. The properties of entropy are studied, and the source-coding theorem for a discrete memoryless source is given. In the second part of the chapter, practical data compression algorithms are studied. Specifically, Huffman coding, which is an optimal data-compression algorithm when the source statistics are known, and Lempel–Ziv (LZ) and Lempel–Ziv–Welch (LZW) coding schemes, which are universal compression algorithms (not requiring the source statistics), are detailed.