InhaltsangabePart 1: Basic spike train statistics: Point process models. Ch 1.- Stochastic models of spike trains-Carl van Vreeswijk. Ch 2.- Estimating the firing rate- Shigeru Shinomoto. Ch 3.- Processing of phase-locked spikes and periodic signals- Go Ashida, Hermann Wagner and Catherine E. Carr. Ch 4.- Analysis and interpretation of interval and count variability in neural spike trains- Martin Paul Nawrot. Part II: Pairwise comparison of spike trains. Ch 5.- Dependence of spike-count correlations on spike-train statistics and observation time-scale- Tom Tetzlaff and Markus Diesmann. Ch 6.- Pair-correlation in the time and frequency domain- Jos J. Eggermont. Ch 7.- Spike metrics- Jonathan D. Victor and Keith P. Purpura. Ch8.- Gravitational clustering- George Gerstein. Part III: Multiple-neuron spike patterns. Ch 9.- Spatio-temporal patterns- Moshe Abeles. Ch 10.- Unitary Events analysis- Sonja Grün, Markus Diesmann and Ad Aertsen. Ch 11.- Information geometry of multiple spike trains- Shun-ichi Amari. Ch 12.- Higher-order correlations and cumulants- Benjamin Staude, Sonja Grün and Stefan Rotter. Part IV: Population-based approaches. Ch13.- Information theory and systems neuroscience- Don H. Johnson, lan N. Goodman and Christopher J. Rozell. Ch 14.- Population coding- Stefano Panzeri, Fernando Montani, Giuseppe Notaro, Cesare Magri and Rasmus S. Petersen. Ch 15. Chastic models for multivariate neural point processes: Collective dynamics and neural decoding- Wilson Truccolo. Part V: Practical issues. Ch 15.- Simulation of stochastic point processes with defined properties-Stefano Cardanobile and Stefan Rotter. Ch 16. Generation and selection of surrogate methods for correlation analysis-Sebastien Louis, Christian Borgelt and Sonja Grün. Ch 17.- Bootstrap tests of hypotheses-Valerie Ventura. Ch18.- Generating random numbers.- Hans Ekkehard Plesser. Ch 19. Practically trivial parallel data processing in a neuroscience laboratory- Michael Denker, Bernd Wiebelt, Denny Fliegner,Markus Diesmann and Abigail Morrison

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