Neuroxidence
Parameters
NeuroXidence

Parameters of the main NeuroXidence function

 

Mandatory parameters:

  1. Input.GDF_all_trials: Contains the spiking activity for individual trials.
  2. e.g. Input.GDF_all_trials{trial} = [ time_spike_1 time_spike_2 time_spike_3; Neuron_1 Neuron_2 Neuron_3]

  3. Input.Window:  defines the window of interest
  4. Input.dt: defines the units in which all tau_c and tau_r are defined, usually 1 ms. e.g. dt=1/1000;

 

Additional parameters that are not mandatory:

  • Input.tau_c:
  • defines the required precision of Joint-spike events in units of Input.dt. e.g. Input.Max_Nr_Jitter_Points=5 and Input.dt= 1/1000 means that spikes belonging to the same Joint-spike events are less than 5 ms apart.
     

  • Input.tau_r:
  • defines the lower bound of time scales considered as rate co-variation in units of Input.dt. e.g. Input.Jitter_for_sig=25 and Input.dt= 1/1000 means that all modulations slower than 25 ms are considered to be rate co-variation and not Joint-spike activity.

  • Input.Requested_Pattern: defines Joint-spike patterns in a binary format that are included irrespective if these patterns exist in the data set
  • Input.Nr_Surrogate: defines the number of surrogates (eta) to compute the p-value
  • Input.Times_of_occurences_flag: If this flag is 1, NeuroXidence lists all times of occurrences for each pattern and each trial.
  • Input.Selected_Neurons: defines the Neurons that should be used.
  • Input.test_level: defines the test level
  • Input.OnlyRequested_flag: If this flag is 1 only those patterns that are defined in Input.Requested_Pattern are considered by Neuroxidence
  • Input.CrossCorr_flag: If this flag is 1 cross-correlations for all pairs of spike trains will be computed. Results will be stored in the Output result structure
  • Input.JitterOrg_flag: If this flag is 1 the original data is jittered the same amount as the surrogate. Therefore this flag can be used to estimate the number of false positives in the case that no synchronization is present. 
  • Input.Pattern_Count_flag: If this flag is 1 the frequency of occurrence per trial and Pattern is stored in the output result structure
  • Input.display_progress: If this flag is 1 the computational progress is displayed
  • Input.WriteLogFileFlag: If this flag is 1 a log file with various information is written
  • Input.VersionTrackFlag: If this flag is 1 all versions of all used sub functions are tracked and listed. Use this flag only for debugging.
     

 

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