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Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanat.

David Pfau: Learning Structure in Time Series for Neuroscience and Beyond

Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data.

Time Series Forecasting Models

His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function.

The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. Springer Professional. Back to the search result list. This way computational models in neuroscience are not only explanat ory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms.

It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. Table of Contents Frontmatter Chapter 1. Statistical Inference Abstract.

Time Series Modeling of Neuroscience Data

This first chapter will briefly review basic statistical concepts, ways of thinking, and ideas that will reoccur throughout the book, as well as some general principles and mathematical techniques for handling these. In this sense it will lay out some of the ground on which statistical methods developed in later chapters rest. The presentation given in this chapter is quite condensed and mainly serves to summarize and organize key facts and concepts required later, as well as to put special emphasis on some topics.

Although this chapter is self-contained, if the reader did not pass through an introductory statistics course so far, it may be advisable to consult introductory chapters in a basic statistics textbook first very readable introductions are provided, for instance, by Hays , or Wackerly et al.

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More generally, it is remarked here that the intention of the first six chapters was more to extract and summarize essential points and concepts from the literature referred to. Assume we would like to predict variables y from variables x through a function f x such that the squared deviations between actual and predicted values are minimized a so-called squared error loss function, see Eq.

Had we also measured more than one output variable, e. In Chap.

Here is a fundamental issue in statistical model fitting or parameter estimation: We usually only have available a comparatively small sample from a much larger population, but we really want to make statements about the population as a whole. Now, if we choose a sufficiently flexible model, e. The problem with this is that it might not say much about the true underlying population anymore as we may have mainly fitted noise—we have overfit the data, and consequently our model would generalize poorly to sets of new observations not used for fitting.

As a note on the side, it is not only the nominal number of parameters relevant for this but also the functional form or flexibility of our model and constraints put on the parameters. For instance, of course we cannot accurately capture a nonlinear functional relationship with a globally linear model, regardless of how many parameters. Or, as noted before, in basis expansions and kernel approaches, the effective number of parameters may be much smaller as the variables are constrained by their functional relationships.

This chapter, especially the following discussion and Sects. In classification approaches as described in Chap. This is also called an unsupervised statistical learning problem. Close Figure Viewer. Browse All Figures Return to Figure.

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Tohru Ozaki (Author of Time Series Modeling of Neuroscience Data)

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