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 intricate. via emphasizing a couple of primary rules, and a handful of ubiquitous options, Analysis of Neural Data presents a unified remedy of analytical equipment that experience turn into crucial for modern researchers. through the booklet rules are illustrated with greater than a hundred examples drawn from the literature, starting from electrophysiology, to neuroimaging, to behaviour. through demonstrating the commonality between a variety of statistical techniques the authors give you the the most important instruments for gaining wisdom from various kinds of information. geared toward experimentalists with in simple terms high-school point arithmetic, in addition to computationally-oriented neuroscientists who've restricted familiarity with data, Analysis of Neural Data serves as either a self-contained advent and a reference work.
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Extra info for Analysis of Neural Data
Mutual information and squared correlation have very different properties. The definition matters, but with either definition we can think of information as producing a reduction of uncertainty. 8 Measuring devices often pre-process the data. Measurements of neural signals are often degraded by noise. A variety of techniques are used to reduce the noise and increase the relative strength of the signal, some of which will be discussed in Chapter 7. In many cases, methods such as these are applied by the measurement software to produce the data the investigator will analyze.
While certain data summaries may be well suited for particular purposes, there is never a uniquely “right” way to collapse the data. A multiplicity of possible data features is inherent to the data analytic process. Furthermore, the details of data summary can be important. , the arithmetic average, of the numbers 2, 3, 10 is 5 while the median is 3. Similarly, a histogram displays the distribution of data values, but the way it does so depends on the way its bins are defined. This is illustrated in the next example.
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