GitHub - ModelDBRepository/149914: Prob. Inference of Short-Term Synaptic Plasticity in Neocort. Microcircuits (Costa et al. 2013)

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Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
+Fitting		+Fitting
+Plasticity		+Plasticity
data		data
toolboxes/@kde		toolboxes/@kde
utils		utils
README.html		README.html
TM_Bayesian.m		TM_Bayesian.m
TM_GUI.m		TM_GUI.m
screenshot.png		screenshot.png
screenshot2.png		screenshot2.png

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*  Probabilistic Inference of Short-term Synaptic Plasticity - inference code  *
*  Model: Tsodyks-Markram (2-4 parameters)				       *
*									       *
*  Version: v0.1 (06/2013)						       *
*  Author: Rui P. Costa (rui.costa@ed.ac.uk)				       *
*  Weblink: <a href="http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=149914">http://senselab.med.yale.edu/modeldb/ShowModel.asp?model=149914</a>    *
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>> How to install:

 1 - Install Matlab (with Statistical Toolbox).
 2 - Open Matlab > Go to STPInference folder

>> How to run:

 1 - Type TM_GUI in the command line
 2 - Open a XLS file (example given in data/examplePCPC.xls) Options:
      Age, frequency and minimum number of pulses.
 3 - Press Run (should take approximately one minute per datapoint) 
 4 - The maximum a posterior (MAP) solutions are given in the main
 window and the respective coefficient of determination (R^2). This is
 a subset of the information in the paper's Fig. 4b and Fig. 6a, only
 for one of the connection types (PC-PC) and a subset of the
 datapoints (n=3):

<img src="./screenshot.png" alt="screenshot">

<img src="./screenshot2.png" alt="screenshot">

 5 - Finally, the plots (posterior and MAP solutions) can be saved in
 PDF format

>> Problems:

   - If the toolbox Kernel Density Estimation (KDE) Toolbox
     (http://www.ics.uci.edu/~ihler/code/kde.html) is not recognised,
     delete the current version, download and compile it in the
     STPInference/toolboxes directory.

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