Real and Modeled Spike Trains: Where do they meet?
Vasile V Moca, Danko Nikolić, Raul C Mureşan
Spike train models are important for the development and calibration of data analysis methods and for the quantification of certain properties of the data. We study here the properties of a spike train model that can produce both oscillatory and non-oscillatory spike trains, faithfully reproducing the firing statistics of the original spiking data being modeled. Furthermore, using data recorded from cat visual cortex, we show that despite the fact that firing statistics are reproduced, the dynamics of the modeled spike trains are significantly different from their biological counterparts. We conclude that spike train models are difficult to use when studying collective dynamics of neurons and that there is no universal recipe for modeling cortical firing, as the latter can be both very complex and highly variable.