Journal
NEURAL COMPUTATION
Volume 23, Issue 12, Pages 3125-3144Publisher
MIT PRESS
DOI: 10.1162/NECO_a_00213
Keywords
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Funding
- MEXT Japan [20300083]
- Next Generation of Physics, Spun from Universality and Emergence, from the MEXT of Japan
- JSPS
- Grants-in-Aid for Scientific Research [20300083, 09J00949] Funding Source: KAKEN
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The time histogram is a fundamental tool for representing the inhomogeneous density of event occurrences such as neuronal firings. The shape of a histogram critically depends on the size of the bins that partition the time axis. In most neurophysiological studies, however, researchers have arbitrarily selected the bin size when analyzing fluctuations in neuronal activity. A rigorous method for selecting the appropriate bin size was recently derived so that the mean integrated squared error between the time histogram and the unknown underlying rate is minimized (Shimazaki & Shinomoto, 2007). This derivation assumes that spikes are independently drawn from a given rate. However, in practice, biological neurons express non-Poissonian features in their firing patterns, such that the spike occurrence depends on the preceding spikes, which inevitably deteriorate the optimization. In this letter, we revise the method for selecting the bin size by considering the possible non-Poissonian features. Improvement in the goodness of fit of the time histogram is assessed and confirmed by numerically simulated non-Poissonian spike trains derived from the given fluctuating rate. For some experimental data, the revised algorithm transforms the shape of the time histogram from the Poissonian optimization method.
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