# doc-cache created by Octave 10.1.0
# name: cache
# type: cell
# rows: 3
# columns: 5
# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
Contents


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1001
 BIOSIG/T330_StimFit contains Matlab/Octave implementation of 
  StimFit [1] and related functions. 

 The following functions are included:
    SIMUL001		generate simulated data (file: test01.gdf)
    SIMUL002		generate simulated data (file: test02.gdf)
    MICROSTIMFIT  	stimfit event analysis 
    MINIDET 		miniature EPSP detection based on template matching		
    DEMO

 REFERENCES: 
 [1] Jose Guzman, Alois Schlögl, Christoph Schmidt-Hieber
     Stimfit: quantifying electrophysiological data with Python.
     Front. Neuroinform. 8:16, 2014
     available online: doi: http://dx.doi.org/10.3389/fninf.2014.00016
     https://pub.ist.ac.at/~schloegl/publications/GuzmanEtAl2014.fninf-08-00016.pdf
 [2] http://pub.ist.ac.at/~schloegl/biosig/
 [3] http://biosig.sf.net/
 [4] https://github.com/neurodroid/stimfit
 [5] Jonas P, Major G, Sakmann B. Quantal components of unitary EPSCs at the mossy
     fibre synapse on CA3 pyramidal cells of rat hippocampus. J Physiol. 1993
     Dec;472:615-63.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 80
 BIOSIG/T330_StimFit contains Matlab/Octave implementation of 
  StimFit [1] ...



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 12
microstimfit


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 3136
 microStimfit for analysis of PSC's in Matlab. 
  It is a re-implementention of Peter Jonas' implementation for Wolfram Mathematica, 
  but it supports also (i) a continues data stream, with (ii) multiple 
  events, and (3) default parameter settings, to allow an easier start. 

 Prerequisite: 
 [1] Biosig for Octave and Matlab
    http://biosig.sourceforge.net/documentation.html
 [2a] Matlab, and Optimization toolbox 
 [2b] Octave, and Optim package

 Usage: 
  [...] = microstimfit(data, samplerate, event_pos, ...)
  [...] = microstimfit(filename, channel, event_pos, ...)
  [results, opt] = microstimfit(filename, channel, event_pos, option)
  [results, opt] = microstimfit(data, samplerate, event_pos, option)

  Input arguments:
      filename 	name of biosig file
      channel 		channel number 
      data 		vector of data samples with 	 
      samplerate	sampling rate of data 
      event_pos	position (in samples) of the event in the data 

      option.t1	start of analysing window [in samples relative to event_pos]
      option.t2	end of analysing window [in samples relative to event_pos]
      option.baseBegin	[in samples relative to event_pos]
      option.baseEnd	[in samples relative to event_pos]
      option.peakBegin	[in samples relative to event_pos]
      option.peakEnd	[in samples relative to event_pos]
      option.meanN	smoothing window, in order to get the peak
      option.dir	direction of peak( 1: up, -1: down, 0: up or down)
      option.plotFlag	1: draw figure and wait for keystrock;	0: no plotting; -1: draw figure and continue
      optioon.baseFlag: 0 (default): mean (average) of baseline
                        1 : median of baseline
      option.fitFlag	0 [default]: no fitting
			1: fit decay with monoexponential model 'a*exp(-x/tau)+offset'
			2: fit decay with biexponential model 'a1*exp(-x/tau1)+a2*exp(-x/tau2) '
			3: fit decay with biexponential model 'a1*exp(-x/tau1)+a2*exp(-x/tau2)+offset'
			5: biexponential with delay: 'a0*exp(delay-x/tau1)-exp(delay-x/tau2)+offset'
      option.thres	threshold value for AP (in relative units or unit voltage or current per sample interval), and
      option.thresFlag	controls the threshold criterion (0 = relative to max slope, 1 = absolute).
      option.fitBegin	start of fitting window (only for fitFlag=5)
      option.fitEnd	data length for fitting, starting from the peak time 

  Output arguments:
	opt		same as option, adding the default values for unspecified option fields
	results.label	cell array, containing the label for each column in results.data
	results.data	table containing the resulting values, NaN indicates the result is not available. 

  Figure plots:
	Crosshair indicates peak, points: 
	black = 0%, red = 20%, green = 80%, blue = 50%, cyan = max dV / dt, magenta = threshold. 

 REFERENCES: 
 [1] Jose Guzman, Alois Schlögl, Christoph Schmidt-Hieber
     Stimfit: quantifying electrophysiological data with Python.
     Front. Neuroinform. 8:16, 2014
     available online: doi: http://dx.doi.org/10.3389/fninf.2014.00016
     https://pub.ist.ac.at/~schloegl/publications/GuzmanEtAl2014.fninf-08-00016.pdf



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 46
 microStimfit for analysis of PSC's in Matlab.



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 7
minidet


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 1147
 Miniature EPSC detection by template matching 

 Usage: 
    [dtrace] = minidet(trace,template)
    [dtrace,a] = minidet(trace,template)
    [results, traceTab, average] = minidet(trace,template,rCrit,aCrit,refract)

  Input: 
    trace :   raw sampling data
    template: default is a bi-exponential template with tauRise=0.5ms, 
              tauDecay=5ms, a 10ms baseline, for data sampled with 20kHz
          template = F([0:0.05:49.9]', 10, .5, 5);
 	        with F defined as            
 	   F = @(t,delta,tauRise,tauDecay) (-double(t>=delta) .* ...
	       (exp((delta-t)/tauRise) - exp((delta-t)/tauDecay) ) / ...
              ( (tauRise/tauDecay)^(tauDecay/(tauDecay-tauRise)) - ...
                  (tauRise/tauDecay)^(tauRise/(tauDecay-tauRise)) ) );
          alternative templates can be described as 
	       template = F([0:1000/SampleRate:49.9]', delay, tauRise, tauDecay);
    rCrit: 	default: 0.5
    aCrit: 	default: 10
    refract: refractory period
  Output:
    dtrace: raw detection trace
    result.tEventList: event times
    traceTab: traces from window [-200,600] samples of each event
    average:  average trace 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 47
 Miniature EPSC detection by template matching 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
simul001


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 40
 simulate EPSC with varying parameters 



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 40
 simulate EPSC with varying parameters 




# name: <cell-element>
# type: sq_string
# elements: 1
# length: 8
simul002


# name: <cell-element>
# type: sq_string
# elements: 1
# length: 425
 simulate EPSC with varying parameters 

 Copyright (C) 2013,2019 Alois Schlögl, IST Austria 

 REFERENCES: 
 [1] Jose Guzman, Alois Schlögl, Christoph Schmidt-Hieber
     Stimfit: quantifying electrophysiological data with Python.
     Front. Neuroinform. 8:16, 2014
     available online: doi: http://dx.doi.org/10.3389/fninf.2014.00016
     https://pub.ist.ac.at/~schloegl/publications/GuzmanEtAl2014.fninf-08-00016.pdf



# name: <cell-element>
# type: sq_string
# elements: 1
# length: 39
 simulate EPSC with varying parameters 





