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288 pp. per issue, 6 x 9,
illustrated
Founded: 1989
ISSN 0899-7667
E-ISSN 1530-888X
2008 ISI Impact Factor: 2.378

Neural Computation

June 2007, Vol. 19, No. 6, Pages 1503-1527
Posted Online April 19, 2007.
(doi:10.1162/neco.2007.19.6.1503)
© 2007 Massachusetts Institute of Technology

A Method for Selecting the Bin Size of a Time Histogram

Hideaki Shimazaki

Shigeru Shinomoto

Department of Physics, Kyoto University, Kyoto 606-8502, Japan



PDF (337.104 KB) PDF Plus (368.348 KB)



The time histogram method is the most basic tool for capturing a time dependent rate of neuronal spikes. Generally in the neurophysiological literature, the bin size that critically determines the goodness of the fit of the time histogram to the underlying spike rate has been subjectively selected by individual researchers. Here, we propose a method for objectively selecting the bin size from the spike count statistics alone, so that the resulting bar or line graph time histogram best represents the unknown underlying spike rate. For a small number of spike sequences generated from a modestly fluctuating rate, the optimal bin size may diverge, indicating that any time histogram is likely to capture a spurious rate. Given a paucity of data, the method presented here can nevertheless suggest how many experimental trials should be added in order to obtain a meaningful time-dependent histogram with the required accuracy.

Cited by

Masami Tatsuno, Jean-Marc Fellous, Shun-ichi Amari. (2009) Information-Geometric Measures as Robust Estimators of Connection Strengths and External Inputs. Neural Computation 21:8, 2309-2335
Online publication date: 1-Aug-2009.
Abstract | Full Text | PDF (223 KB) | PDF Plus (177 KB) 
Benjamin Staude, Stefan Rotter, Sonja Grün. (2008) Can Spike Coordination Be Differentiated from Rate Covariation?. Neural Computation 20:8, 1973-1999
Online publication date: 1-Aug-2008.
Abstract | PDF (171 KB) | PDF Plus (189 KB) 

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