Neural Comput - Improved similarity measures for small sets of spike trains.

Tópicos

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Resumo

Multiple measures have been developed to quantify the similarity between two spike trains. These measures have been used for the quantification of the mismatch between neuron models and experiments as well as for the classification of neuronal responses in neuroprosthetic devices and electrophysiological experiments. Frequently only a few spike trains are available in each class. We derive analytical expressions for the small-sample bias present when comparing estimators of the time-dependent firing intensity. We then exploit analogies between the comparison of firing intensities and previously used spike train metrics and show that improved spike train measures can be successfully used for fitting neuron models to experimental data, for comparisons of spike trains, and classification of spike train data. In classification tasks, the improved similarity measures can increase the recovered information. We demonstrate that when similarity measures are used for fitting mathematical models, all previous methods systematically underestimate the noise. Finally, we show a striking implication of this deterministic bias by reevaluating the results of the single-neuron prediction challenge.

Resumo Limpo

multipl measur develop quantifi similar two spike train measur use quantif mismatch neuron model experi well classif neuron respons neuroprosthet devic electrophysiolog experi frequent spike train avail class deriv analyt express smallsampl bias present compar estim timedepend fire intens exploit analog comparison fire intens previous use spike train metric show improv spike train measur can success use fit neuron model experiment data comparison spike train classif spike train data classif task improv similar measur can increas recov inform demonstr similar measur use fit mathemat model previous method systemat underestim nois final show strike implic determinist bias reevalu result singleneuron predict challeng

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