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INNOVATIVE METHODOLOGY

Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics

¹Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital; ²Department of Mathematics and Statistics, Boston University, Boston; ³Division of Health Sciences and Technology, Harvard Medical School/Massachusetts Institute of Technology; ⁴Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts; ⁵Institut des Sciences Cognitives, Centre National de la Recherche Scientifique, Bron, France; and ⁶Center for Neural Science, New York University, New York, New York

Submitted 27 March 2007; accepted in final form 17 January 2008

Recording single-neuron activity from a specific brain region across multiple trials in response to the same stimulus or execution of the same behavioral task is a common neurophysiology protocol. The raster plots of the spike trains often show strong between-trial and within-trial dynamics, yet the standard analysis of these data with the peristimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. By itself, the PSTH does not provide a framework for statistical inference. We present a state-space generalized linear model (SS-GLM) to formulate a point process representation of between-trial and within-trial neural spiking dynamics. Our model has the PSTH as a special case. We provide a framework for model estimation, model selection, goodness-of-fit analysis, and inference. In an analysis of hippocampal neural activity recorded from a monkey performing a location-scene association task, we demonstrate how the SS-GLM may be used to answer frequently posed neurophysiological questions including, What is the nature of the between-trial and within-trial task-specific modulation of the neural spiking activity? How can we characterize learning-related neural dynamics? What are the timescales and characteristics of the neuron's biophysical properties? Our results demonstrate that the SS-GLM is a more informative tool than the PSTH and ANOVA for analysis of multiple trial neural responses and that it provides a quantitative characterization of the between-trial and withintrial neural dynamics readily visible in raster plots, as well as the less apparent fast (1–10 ms), intermediate (11–20 ms), and longer (>20 ms) timescale features of the neuron's biophysical properties.