The Spike Train Analysis toolbox is the third of four NeuroMAX
Toolboxes, which also include Spike Detection, Spike Sorting,
and Display Tools. After detecting individual spikes (with
the Spike Detection toolbox), and sorting the spikes into
units (with the Spike Sorting tools), you can analyze the
spike times of specific units with the Spike Train Analysis
tools.
The various Spike Train Analysis tools are summarized below.
Select a specific tool for a complete description of its
strengths in analyzing spike train data.
StationarityTest (Under Development):
Stationarity is the fundamental assumption behind all of
the point process analyses found in the Spike Train Analysis
toolbox. Refer to this tool for details. (A point process
is a random collection of points, where each point represents
the time and location of an event.)
ConditionalMean (Under Development): Examines
whether or not the inter-event intervals have a first order
dependence structure.
Hazard (Under Development): Gives the
value of the interval histogram divided by the sum of the
interval histogram * the binwidth at each bin.
IntervalHist: Estimates the probability
density function of inter-event interval durations.
SinglePST: Creates a
Post Stimulus Time histogram for data collected from an
individual neural unit synched to a repeated stimulus.
JointPST: The analysis
code from MULAB computes the Joint Peristimulus Time
Histogram (JPSTH). This analysis allows a user to investigate
the correlation structure
between the responses of two different neurons to the
same repeated stimulus.
Details of the analysis are explained at the Mulab website: http://mulab.physiol.upenn.edu/analysis.html
CostBasedDistSU: Implements the “cost-based” metric
for single units by calculating the “spike time distance”,
D[q] between two spike trains.
CostBasedDistMU: Implements the “cost-based” metric
for multi-neuronal spike times by
calculating the “spike time distance”, D[q,k] between
two multiunit spike trains.
FitPoisson: Fits a Poisson1 mode to each
unit ’s event times.
GeneratePoisson: Generates spike times
based on a Poisson1 model.
CreateSimulation (Under Development):
Creates a simulated data set based on a specific data sample,
using the available information about a data set (noise
samples or models, identified unit spike waveform templates,
and unit firing characteristics) to generate a simulated
data set that closely resembles a specific data set.
1 A discrete probability distribution that expresses
the probability of a number of events occurring in a fixed
period of time if these events occur with a known average
rate, and are independent of the time since the last event.
Wikipedia contributors, “Poisson distribution,”
Wikipedia, The Free Encyclopedia, http://en.wikipedia.org/w/index.php?title=Poisson_distribution&oldid=157057364
(accessed September 10, 2007).
