Analysis of Neural Data by Robert E. Kass, Uri T. Eden, Emery N. Brown

By Robert E. Kass, Uri T. Eden, Emery N. Brown

Continual advancements in info assortment and processing have had a big impact on mind learn, generating facts units which are frequently huge and complex. via emphasizing a number of basic ideas, and a handful of ubiquitous recommendations, Analysis of Neural Data offers a unified remedy of analytical equipment that experience develop into crucial for modern researchers. in the course of the booklet principles are illustrated with greater than a hundred examples drawn from the literature, starting from electrophysiology, to neuroimaging, to behaviour. via demonstrating the commonality between a variety of statistical techniques the authors give you the an important instruments for gaining wisdom from different different types of information. geared toward experimentalists with merely 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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18 1 Introduction Scientific Issue Experiment Exploratory Analysis Statistical Model Assessment of Fit: Adequate? NO YES Statistical Inference Scientific Conclusion Fig. 7 Formal statistical inference within the process of drawing scientific conclusions. Statistical model building is a prerequisite to formal inference procedures. Model building is iterative in the sense that tentative models must be assessed and, if necessary, improved or abandoned. The figure is something of a caricature because the process is not as neat as depicted here.

Another motivation for logarithmic transformations is that they convert power laws, which are useful in describing many neuroscientific phenomena, to simpler linear forms. 2) and may be summarized by saying that a proportional change in v produces a proportional change in w. If we let y = log w and x = log v then Of all the values A in the function f (x) = A x , the value A = e makes the derivative of f (x) exactly equal to f (x) itself. For other values of A a constant must be introduced, which would make calculus-based formulas more complicated.

5. That example involved a linear relationship. As we note in Chapters 14 and 15, the methods used to fit linear models can be generalized for nonlinear relationships. The methods in Chapter 15 are also called nonparametric because the fitted relationship is not required to follow a pre-specified form. 6 Excitatory post-synaptic current As part of a study on spike-timingdependent plasticity (Dr. 2 The Contribution of Statistics 15 Fig. 5 Excitatory post-synaptic current. Current recorded from a rat hippocamal neuron, together with smoothed version (shown as the thin line within the noisy current trace) obtained by fitting a suitable function of time, given in the text.

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