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Keywords: Spike detection; Spike sorting; Spike train analysis; Extra-cellular recording; Tetrode; Electrophysiology; Multi-unit recording; Single-unit recording; Cluster analysis; Superposition resolution; MATLAB; Accuracy quantification; Object Oriented Programming;
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Spike Train Analysis Toolbox: Overview                     



Description

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).

 
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Last Updated: 11-Nov-2008