Neural Comput - Optimizing time histograms for non-Poissonian spike trains.

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Resumo

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.

Resumo Limpo

time histogram fundament tool repres inhomogen densiti event occurr neuron fire shape histogram critic depend size bin partit time axi neurophysiolog studi howev research arbitrarili select bin size analyz fluctuat neuron activ rigor method select appropri bin size recent deriv mean integr squar error time histogram unknown under rate minim shimazaki shinomoto deriv assum spike independ drawn given rate howev practic biolog neuron express nonpoissonian featur fire pattern spike occurr depend preced spike inevit deterior optim letter revis method select bin size consid possibl nonpoissonian featur improv good fit time histogram assess confirm numer simul nonpoissonian spike train deriv given fluctuat rate experiment data revis algorithm transform shape time histogram poissonian optim method

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