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highly plastic self-organizing recurrent network model to analyse survivability of transient synapses

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Network simulation using Brian 2 and Pypet written to analyse the survivability of transient synapses of cortical neurons.

Requirements

To run the network simulation a Python 3 environment with Brian 2, and Pypet is required. Git also has to be installed.

Any recent version of Brian 2 should work. However, to run very long simulations a fix discussed in #1057 is required.

Below is an example installation routine for setting up a virtualenv (tested on Ubuntu 16.04 only).

#!/bin/sh

virtualenv -p python3 /home/$USER/env/synsrv
source /home/$USER/env/synsrv/bin/activate

# Brian 2 branch that fixes bug for long simulations
pip install -e git+https://github.com/brian-team/brian2@355cbf98061f2ae4c4afdc1600f363c5d8c0979d#egg=Brian2

pip install mrestimator==0.1.5
pip install elephant==0.6.0

pip install tables==3.5.1
pip install pypet==0.4.3

pip install GitPython

Installation

Get the code from this repository including submodules with

git clone  --recurse-submodules https://github.com/synsrv/src_net

Run a simulation

To verify your installation, within src_net run

./run_test.sh -l tests/net_tests/test_standard_net/

This will create a directory tests/ on the same level as src_net where the code is copied an executed.

Explore parameters

To run simulations exploring different parameter sets, create an explored_params.py in src_net/net/, as for example found in any of the tests in net_tests/. In the input_dict of explored_params.py specify the parameter setting of your choice and the runn simulation via

./run.sh -l -n 2 -P "_two-concurrent-sims" -E

This will create running/ and completed/ directories on the same level as src_net/. Once finished, the simulation directory is moved from running/ to completed/.

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highly plastic self-organizing recurrent network model to analyse survivability of transient synapses

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