By Robert E. Kass, Uri T. Eden, Emery N. Brown
Continual advancements in facts assortment and processing have had a big impact on mind examine, generating facts units which are frequently huge and complex. by means of emphasizing a number of basic ideas, and a handful of ubiquitous innovations, Analysis of Neural Data presents a unified therapy of analytical equipment that experience develop into crucial for modern researchers. during the e-book rules are illustrated with greater than a hundred examples drawn from the literature, starting from electrophysiology, to neuroimaging, to behaviour. by means of demonstrating the commonality between numerous statistical ways the authors give you the the most important instruments for gaining wisdom from diversified sorts of info. aimed toward experimentalists with in simple terms high-school point arithmetic, in addition to computationally-oriented neuroscientists who've constrained familiarity with data, Analysis of Neural Data serves as either a self-contained creation and a reference work.
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Extra info for Analysis of Neural Data
Thus, probability modeling and model assessment are iterative, and only when a model is considered adequate are statistical inferences made. This process is embedded into the production of scientific conclusions from experimental results (Box et al. 1978). 5 All models are wrong, but some are useful. The simple representation in Fig. 7 is incomplete and may be somewhat misleading. Most importantly, while it is true that there are standard procedures for model assessment, some of which we will discuss in Chapter 10, there is no uniformly-applicable rule for what constitutes a good fit.
Sometimes the second subset involves entirely new data. For example, in a behavioral study, a new set of subjects may be recruited and examined. Methods that perform well with this kind of cross-validation are often quite compelling. In addition to being intuitive, cross-validation has a theoretical justification discussed briefly in Chapter 12. 7 Important data analytic ideas are sometimes implemented in many different ways. 1. There are three reasons for putting a discussion of central tendency at the beginning.
Another general point about the statistical paradigm is illustrated in Fig. 7. This figure shows where the statistical work fits in. Real investigations are far less sequential than depicted here, but the figure does provide a way of emphasizing two components of the process that go hand-in-hand with statistical modeling: exploratory analysis and assessment of fit. Exploratory analysis involves informal investigation of the data based on numerical or graphical summaries, such as a histogram. Exploratory results, together with judgment based on experience, help guide construction of an initial probability model to represent variability in observed data.
Analysis of Neural Data by Robert E. Kass, Uri T. Eden, Emery N. Brown