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BEGIN:VEVENT
DTSTAMP:20260629T031632Z
DTSTART;VALUE=DATE-TIME:20071114T150000
DTEND;VALUE=DATE-TIME:20071114T160000
SUMMARY:Comp Bio Seminar: Neural spiking dynamics in learning tasks
TZID:Europe/London
UID:20071114-094d43af16005e4901161fa15b132c5c@warwick.ac.uk
CREATED:20071108T141936Z
DESCRIPTION:Speaker: Gabriela Czanner (ÌÇÐÄTV Medical School & ÌÇÐÄTV M
 anufacturing Group) (https://neurostat.mgh.harvard.edu/gabriela/Gabriela
 CzannerHomepage.htm) Abstract: Recording single neuron activity from a s
 pecific brain region across multiple trials in response to the same stim
 ulus or execution of the same behavioral task is a common neurophysiolog
 y protocol. The raster plots of the spike trains often show that the neu
 rons have strong between-trial and within-trial dynamics\, yet the stand
 ard analysis of these data with the perstimulus time histogram (PSTH) an
 d ANOVA do not consider between-trial dynamics. Further\, by itself\, th
 e PSTH does not provide a framework for statistical inference. We presen
 t a state-space generalized linear model (SS-GLM) to formulate a point p
 rocess representation of between-trial and within-trial neural spiking d
 ynamics for multiple trial neurophysiology experiments. Our formulation 
 of the SS-GLM has the PSTH as a special case. We provide a likelihood-ba
 sed framework for model estimation\, model selection\, goodness-of-fit a
 nalysis and inference. In an analysis of hippocampal neural activity rec
 orded from a monkey performing a location-scene association task\, we de
 monstrate how the SS-GLM may be used to answer frequently posed neurophy
 siological questions including\, What is the nature of the between-trial
  and within-trial task-specific modulation of the neural spiking activit
 y? How can we characterize learning-related neural dynamics? What are th
 e time-scales and characteristics of the neuron’s biophysical properties
 ? Our results demonstrate that the SS-GLM is a more informative tool tha
 n the PSTH and ANOVA for analysis of multiple trial neural responses tha
 t provides a quantitative characterization of the between-trial and with
 in-trial neural dynamics readily visible in raster plots\, as well as th
 e less apparent features of the neuron’s biophysical properties.
LOCATION:
CATEGORIES:
LAST-MODIFIED:20071108T141936Z
ORGANIZER;CN=Sara Kalvala:
END:VEVENT
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