@book{endres_bayesian_2007,
	title = {Bayesian binning beats approximate alternatives: {Estimating} peristimulus time histograms},
	volume = {20},
	shorttitle = {Bayesian binning beats approximate alternatives},
	abstract = {The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiol- ogists. The former is usually obtained by binning spike trains, whereas the stan- dard method for the latter is smoothing with a Gaussian kernel. Selection of a bin width or a kernel size is often done in an relatively arbitrary fashion, even though there have been recent attempts to remedy this situation (1, 2). We develop an exact Bayesian, generative model approach to estimating PSTHs and demonstate its superiority to competing methods. Further advantages of our scheme include automatic complexity control and error bars on its predictions.},
	author = {Endres, Dominik and Oram, Mike and Schindelin, Johannes and Földiák, Peter},
	month = jan,
	year = {2007},
	note = {Journal Abbreviation: Adv Neural Inf Process Syst
Publication Title: Adv Neural Inf Process Syst},
}