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To understand how neural networks process information, it is important toinvestigate how neural network dynamics varies with respect to differentstimuli. One challenging task is to design efficient statistical approaches toanalyze multiple spike train data obtained from a short recording time. Basedon the development of high-dimensional statistical methods, it is able to dealwith data whose dimension is much larger than the sample size. However, thesemethods often require statistically independent samples to start with, whileneural data are correlated over consecutive sampling time bins. We develop anapproach to pretreat neural data to become independent samples over time bytransferring the correlation of dynamics for each neuron in different samplingtime bins into the correlation of dynamics among different dimensions withineach sampling time bin. We verify the method using simulation data generatedfrom Integrate-and-fire neuron network models and a large-scale network modelof primary visual cortex within a short time, e.g., a few seconds. Our methodmay offer experimenters to use the advantage of the development of statisticalmethods to analyze high-dimensional neural data.

Swift Two-sample Test on High-dimensional Neural Spiking Data