Abstract
Although it is well understood that neural systems receive, transmit, and process information about the outside world through synchronized spiking activity, many analyses of spike trains ignore the fundamental statistical structure in this data. Spikes are localized events in time that can carry information based on their frequency, their precise timing, their rhythmic dynamics, or their coordination across a neural population. For this reason, spike train data often violates the assumptions underlying classical statistical methods. The theory of point processes offers a unified, principled approach to modeling the firing properties of spiking neural systems, and assessing goodness-of-fit between a neural model and observed spiking data. In this article, we will summarize some important aspects of this theory and show how point process methods are used to model spiking data from individual neurons and neural populations.
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Eden, U.T., Kass, R.E. (2016). Statistical Models of Spike Train Data. In: Pfaff, D., Volkow, N. (eds) Neuroscience in the 21st Century. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6434-1_167-1
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DOI: https://doi.org/10.1007/978-1-4614-6434-1_167-1
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