Mark Kramer

Robert E. Kass

Sonja Grün

Markus Diesmann

Peter J. Thomas

Shun-Ichi Amari

Kensuke Arai

Emery Brown

Casey O. Diekman

Brent Doiron

Uri Eden

Adrienne Fairhall

Grant M. Fiddyment

Tomoki Fukai

Matthew T. Harrison

Moritz Helias

Hiroyuki Nakahara

Jun-nosuke Teramae

Mark Reimers

Jordan Rodu

Horacio G. Rotstein

Eric Shea-Brown

Hideaki Shimazaki

Shigeru Shinomoto

Byron M. Yu

Mathematical and statistical models have played important roles in neuroscience, especially by describing the electrical activity of neurons recorded individually, or collectively across large networks. As the field moves forward rapidly, new challenges are emerging. For maximal effectiveness, those working to advance computational neuroscience will need to appreciate and exploit the complementary strengths of mechanistic theory and the statistical paradigm.

Mathematical and Statistical Perspectives

Computational Neuroscience

Stefan Rotter

Solid and transparent data analysis is the most important basis for reliable interpretation of experiments. The technique of parallel spike train recordings using multi-electrode arrangements has been available for many decades now, but only recently gained wide popularity among electro physiologists. Many traditional analysis methods are based on firing rates obtained by trial-averaging, and some of the assumptions for such procedures to work can be ignored without serious consequences. The situation is different for correlation analysis, the result of which may be considerably distorted if certain critical assumptions are violated. The focus of this book is on concepts and methods of correlation analysis (synchrony, patterns, rate covariance), combined with a solid introduction into approaches for single spike trains, which represent the basis of correlations analysis. The book also emphasizes pitfalls and potential wrong interpretations of data due to violations of critical assumptions.

Analysis of Parallel Spike Trains

Sonja Grün

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