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

@article{citeulike:2797291, abstract = {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. 10.1152/jn.00343.2007}, author = {Czanner, Gabriela and Eden, Uri T. and Wirth, Sylvia and Yanike, Marianna and Suzuki, Wendy A. and Brown, Emery N. }, citeulike-article-id = {2797291}, doi = {10.1152/jn.00343.2007}, journal = {J Neurophysiol}, keywords = {analysis, in-vivo, learning, methods, time}, month = {May}, number = {5}, pages = {2672--2693}, posted-at = {2008-05-14 10:52:52}, priority = {2}, title = {Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics}, url = {http://dx.doi.org/10.1152/jn.00343.2007}, volume = {99}, year = {2008} }

TY - JOUR ID - citeulike:2797291 L3 - citeulike-article-id:2797291 TI - Analysis of Between-Trial and Within-Trial Neural Spiking Dynamics JF - J Neurophysiol VL - 99 IS - 5 SP - 2672 EP - 2693 N2 - 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. 10.1152/jn.00343.2007 KW - analysis KW - in-vivo KW - learning KW - methods KW - time AU - Czanner, Gabriela AU - Eden, Uri AU - Wirth, Sylvia AU - Yanike, Marianna AU - Suzuki, Wendy AU - Brown, Emery PY - 2008/05/01/ UR - http://dx.doi.org/10.1152/jn.00343.2007 ER -