title: Analysis of between-trial and within-trial neural spiking dynamics.
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 within-trial 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.
authors.affiliation: Neuroscience Statistics Research Laboratory, Department of Anesthesia and Critical Care, Massachusetts General Hospital, Boston, MA, USA. g.czanner@warwick.ac.uk
authors.forename: Gabriela
authors.initials: G
authors.lastname: Czanner
authors.forename: Uri T
authors.initials: UT
authors.lastname: Eden
authors.forename: Sylvia
authors.initials: S
authors.lastname: Wirth
authors.forename: Marianna
authors.initials: M
authors.lastname: Yanike
authors.forename: Wendy A
authors.initials: WA
authors.lastname: Suzuki
authors.forename: Emery N
authors.initials: EN
authors.lastname: Brown
mesh_terms: D000465:Algorithms; D000818:Animals; D003627:Data Interpretation, Statistical; D004558:Electric Stimulation; D004594:Electrophysiology / Q000379:methods* / Q000706:statistics & numerical data; D006624:Hippocampus / Q000166:cytology / Q000502:physiology; D016014:Linear Models; D008253:Macaca mulatta; D008959:Models, Neurological; D009474:Neurons / Q000502:physiology; D015203:Reproducibility of Results