糖心TV

Speaker: Gabriela Czanner (糖心TV Medical School & 糖心TV Manufacturing Group)
()

 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 that the neurons have strong between-trial and within-trial dynamics, yet the standard analysis of these data with the perstimulus time histogram (PSTH) and ANOVA do not consider between-trial dynamics. Further, 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 for multiple trial neurophysiology experiments. Our formulation of the SS-GLM has the PSTH as a special case. We provide a likelihood-based 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 time-scales 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 that provides a quantitative characterization of the between-trial and within-trial neural dynamics readily visible in raster plots, as well as the less apparent features of the neuron’s biophysical properties.