These BRIAN/NEST/NEURON scripts associated with paper (manuscript):


Ruben A. Tikidji-Hamburyan, Vikram Narayana, Zeki Bozkus, and Tarek A. El-Ghazawi

Software for Brain Network Simulations: A Comparative Study

We deeply appreciate the comments of reviewer Marcel Stimberg, which allowed us to significantly improve the quality of the code.

To use theses scripts you need python libraries:

  1. numpy

  2. scipy

  3. matplotlib and LaTeX for correct graphical interface


and simulators:

  1. NEURON Release 7.4 (1370:16a7055d4a86) 2015-11-09

  2. NEST version 2.12.0.

  3. BRIAN version 2.0.1

Under Ubuntu or any other Debian based Linux, run
sudo apt-get install python-numpy python-scipy python-matplotlib texlive-full cython.

You can use yum or zymm under RadHad or SUSE based Linux distributions.

You may also use sudo pip install numpu scipy matloblib cython in any distribution.

Follow instructions for installation NEURON, NEST and BRIAN simulators.

Case Study 1

To obtain results of the Case Study 1, cd into directory CaseStudy1-5000LIF

cd CaseStudy1-5000LIF

and run simulations

./Benchmark.sh

The raster diagrams for each software will be shown.



BRIAN

NEST

NEURON



Statistics of software performance and number of lines and characters in corresponding modules and scripts will printed out in standard output. This statistics was used to obtained results of the case study.

Files in this directory:

Benchmark.sh – script helper
BenchmarkAndMemory.sh – same as Benchmark.sh but with memory usage estimation.
ee.ssv - E-E neuron connectivity
ei.ssv - E-I neuron connectivity
ie.ssv - I-E neuron connectivity
ii.ssv - I-I neuron connectivity
se.ssv - S-E neuron connectivity
input.ssv - input pattern
./BRIAN/example.py – script for BRIAN
./NEST/example.py – script for NEST
./NEST/iaf_psc_delta.h – module of LIAF, header (*)
./NEST/iaf_psc_delta.cpp – module of LIAF, c++ code (*)
./NEURON/example.py – script for NEURON
./NEURON/intfire1.mod – module of LIAF (*)
./NEURON/vecevent.mod – module for input generator

Case Study 2

Note.

NEST module hh_psc_alpha does NOT completely implement the system of differential equations (2) in the paper. This module has a synaptic model with an injected current instead of a model with reversal potential and conductance. The difference slightly reduces the computational load for the NEST benchmark and does not affect any results of Case Study 2. This inaccuracy was found after the publication. The authors have posted this note to prevent further confusion.

To obtain results of the Case Study 2, cd into directory CaseStudy2-400HH

cd CaseStudy2-400HH

and run simulations

./Benchmark.sh

The raster diagrams for each software will be shown.



BRIAN

NEST

NEURON



Statistics of software performance and number of lines and characters in corresponding modules and scripts will printed out in standard output. This statistics was used to obtained results of the case study.

Files in this directory:


Benchmark.sh – script helper
BenchmarkAndMemory.sh – same as Benchmark.sh but with memory usage estimation.
connections.ssv - network connectivity
volt.ssv – voltage initial condition
./BRIAN/example.py – script for BIRAK
./NEST/example.py – script for NEST
./NEST/hh_psc_alpha.h – module of HH, header (*)
./NEST/hh_psc_alpha.cppmodule of HH, c++ code (*)
./NEURON/example.py – script for NEURON
./NEURON/expsyn.mod – module of double exponential synapses (*)
./NEURON/exp2syn.mod – module of double exponential synapses (*)
./NEURON/hh.mod – module for HH currents (*)

(*) files are not required for simulation, but used for code complexity study. These files were copied from NEURON/NEST source code and cleaned from comments, empty lines etc.