GitHub - ModelDBRepository/239427: Feedforward network undergoing Up-state-mediated plasticity (Gonzalez-Rueda et al. 2018)

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Feedforward network undergoing Up-state-mediated plasticity (Gonzalez-Rueda et al. 2018)

modeldb.science/239427

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Name		Name	Last commit message	Last commit date
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Figure4CD		Figure4CD
Figure4E		Figure4E
README.html		README.html

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<html><pre>
<h1>Activity-dependent downscaling of subthreshold synaptic 
inputs during slow wave sleep-like activity in vivo </h1>

<h2>Ana Gonzalez-Rueda, Victor Pedrosa, Rachael Feord, 
Claudia Clopath and Ole Paulsen </h2>
<hr>

<h2>General description </h2>
Simulates a feedforward network of excitatory neurons as in:

[1] González-Rueda, A., Pedrosa, V., Feord, R., Clopath, C., Paulsen, O. Activity-dependent 
downscaling of subthreshold synaptic inputs during slow wave sleep-like activity in vivo. 
Neuron (2018).

Code written by: Victor Pedrosa <a href="mailto:v.pedrosa15@imperial.ac.uk?Subject=Hello%20again" target="_top">v.pedrosa15@imperial.ac.uk</v.pedrosa15@imperial.ac.uk></a>
Imperial College London, London, UK - Dec 2017


<h2>Figure 4CD</h2>
<h3>List of files</h3>
(1) run_code.py
This file runs all the code in steps 1,2 and 3, generating the data in 'Data/' and the 
figures in 'Figures/'.

(2) Step1-wake_learning/UP-state-mediated_plast_fig4CD_wake.py
Simulates a feedforward network of integrate-and-fire neurons with plastic excitatory
synapses. The synapses are updated following a standard STDP rule and some neurons receive
50% stronger currents than the others. This code uses functions and parameters in 
SimStep.py and params.py. 

(3) Step2-sleep_learning/UP-state-mediated_plast_fig4CD_sleep.py
Simulates a feedforward network of integrate-and-fire neurons with plastic excitatory
synapses. Synaptic weights are initiated as the final weights from (1). Those weights are 
updated followin the Up-state-mediated plasticity described in [1]. This code uses 
functions and parameters in SimStep.py and params.py. 

(4) Step3-figures/Make_fig4CD.py
Plots and save the figure generated with the data produced from (1) and (2). Figures are 
saved in Figures/.


<h3>To simulate the network and plot the figures  </h3>

1. run (1): simulates the network, saves the results and generate figure 4CD (below);


<img src="./Figure4CD/Figures/fig4CD.png" alt="Figure 1" width="550">



<h2>Figure 4E</h2>
<h3>List of files</h3>
(1) run_code.py
This file runs UP-state-mediated_plast_fig4E for 200 trials, which creates all the data 
in Data/

(2) UP-state-mediated_plast_fig4E.py
Simulates the network and saves the data in Data/

(3) Make_fig4E.py
Gets the data in Data/ as input, generate the figure and save it in Figures/

(4) SimStep.py
Functions to be used in each integration time step. These fundtions are called from (2)

(5) params.py
Parameters used by (2)


<h3>To simulate the network and plot the figures  </h3>

1. run (1): simulates the network, saves the results and generate figure 4E (below);


<img src="./Figure4E/Figures/fig4E.png" alt="Figure 1" width="350">