DOI: 10.7910/dvn/xuyxkc
title: Replication Data for: Model-driven Analysis of Eyeblink Classical Conditioning Reveals the Underlying Structure of Cerebellar Plasticity and Neuronal Activity

description: The data are related to the following article:
&lt;br&gt;
1. Antonietti A., Casellato C., D'Angelo E. and Pedrocchi A. (2016)
Model-driven Analysis of Eyeblink Classical Conditioning Reveals
the Underlying Structure of Cerebellar Plasticity and Neuronal Activity.
IEEE Transactions on Neural Networks and Learning Systems (TNNLS)
DOI: 10.1109/TNNLS.2016.2598190
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&lt;br&gt;
&lt;b&gt; Abstract &lt;/b&gt;
&lt;br&gt;
The cerebellum plays a critical role in sensorimotor control. However,
how the specific circuits and plastic mechanisms of the cerebellum are 
engaged in closed-loop processing is still unclear. We developed an 
artificial sensorimotor control system embedding a detailed spiking cerebellar 
microcircuit with three bidirectional plasticity sites. This proved able 
to reproduce a cerebellar-driven associative paradigm, the Eye Blink 
Classical Conditioning (EBCC), in which a precise time relationship between 
an unconditioned and a conditioned stimulus (US and CS) is established. 
We challenged the spiking model to fit an experimental dataset from human 
subjects. Two subsequent sessions of EBCC acquisition and extinction were 
recorded and Transcranial Magnetic Stimulation (TMS) was applied on the 
cerebellum to alter circuit function and plasticity. Evolutionary algorithms 
were used to find the near optimal model parameters to reproduce the 
behaviors of subjects in the different sessions of the protocol. The main finding is that the optimized cerebellar model was able to learn 
to anticipate (predict) conditioned responses with accurate timing and 
success rate, demonstrating fast acquisition, memory stabilization, 
rapid extinction, and faster re-acquisition as in EBCC in humans. 
The firing of Purkinje cells (PC) and Deep Cerebellar Nuclei (DCN) changed
 during learning under the control of synaptic plasticity, which evolved at 
different rates, with a faster acquisition in the cerebellar cortex than in DCN 
synapses. Eventually, a reduced PC activity released DCN discharge just after the CS, precisely anticipating the US and causing the eyeblink. 
Moreover, a specific alteration in cortical plasticity explained the 
EBCC changes induced by cerebellar Transcranial Magnetic Stimulation (TMS)
 in humans. In this paper, for the first time, it is shown how closed-loop 
simulations, using detailed cerebellar microcircuit models, can be 
successfully used to fit real experimental datasets. Thus, the changes of 
the model parameters in the different sessions of the protocol unveil how 
implicit microcircuit mechanisms can generate normal and altered associative 
behaviors.
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&lt;br&gt;
&lt;b&gt;Usage:&lt;/b&gt;
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The simulations were performed with a Desktop PC with Windows 7 64 bit
and MATLAB 2016a 64-bit with the Parallel Computing Toolbox. With the
following files you can run the simulations to reproduce the data analyzed
in the article and to reproduce some figures. &lt;br&gt;
If you want more information about the EDLUT simulator, you can see
https://code.google.com/archive/p/edlut/
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&lt;br&gt;
All the files need to be in the directory where the simulation is launched.
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Open with MATLAB the file Reproduce_Simulations.m and run it. It will check
if all the files are present and it will run the simulations for the models.
After you can generate the different figures and results opening the scripts
Figure2.m, Figure4.m, Figure5.m.
&lt;br&gt;
&lt;br&gt;
&lt;b&gt;The following files are included:&lt;/b&gt;
&lt;br&gt;
- DataSet.mat: includes the optimal parameters of the three different
 situations (session1, session2_sham, session2_tms) found by the Genetic
 Algorithm optimization process.
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- Reproduce_Simulations.m: with this file it is possible to generate the
 simulations with each of the parameters' set. It will generate a folder
 for each model. The generation 1 models are the models of session1, the
 generation 2 models are the models of session2_sham and the generation 3
 models are the models of session2_tbs. Finally, this script reproduces Table II. Sensibility and Specificity tests. 
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- Figure2.m: reproduces Figure 2.B and computes the computational times
 needed for the simulations
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- Figure4.m: reproduces Figure 4. Since this script has to load all the
 weights files, it could take some minutes to be done.
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- Figure5.m: reproduces Figure 5. The computational load of this script is
 quite intensive and it will take several minutes to generate all the images.
&lt;br&gt;
- EBCC_s1.exe and EBCC_s2.exe: are the executable files that run the simulations
 given the simulation parameters in the proper files generated by the
 MATLAB script (file_param.dat and WeightsEBCC_X.dat)
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- NetworkEBCC_GA_new.cfg: The network description files for EDLUT simulator.
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- WeightsEBCC_9999.dat: The initialization synaptic weights file for sessions2 simulations
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- Auxiliary MATLAB functions (figureFullScreen.m, median_p.m, stdshade_median.m)
 that are used to plot the results.
&lt;br&gt;
&lt;br&gt;
These model files were supplied by Alberto Antonietti. If you have any
question/comments/feedback, please contact me.
description: EDLUT, 2.0
description: MATLAB, 2015a