Brain networks simulators - a comparative study (Tikidji-Hamburyan et al 2017)

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" ... In this article, we select the three most popular simulators, as determined by the number of models in the ModelDB database, such as NEURON, GENESIS, and BRIAN, and perform an independent evaluation of these simulators. In addition, we study NEST, one of the lead simulators of the Human Brain Project. First, we study them based on one of the most important characteristics, the range of supported models. Our investigation reveals that brain network simulators may be biased toward supporting a specific set of models. ... we carry out an evaluation using two case studies: a large network with simplified neural and synaptic models and a small network with detailed models. These two case studies allow us to avoid any bias toward a particular software package ..."
1 . Tikidji-Hamburyan RA, Narayana V, Bozkus Z, El-Ghazawi TA (2017) Software for Brain Network Simulations: A Comparative Study Front. Neuroinform.
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Model Information (Click on a link to find other models with that property)
Model Type:
Brain Region(s)/Organism:
Cell Type(s):
Gap Junctions:
Simulation Environment: Brian; NEST; NEURON; GENESIS; Python;
Model Concept(s): Methods;
Implementer(s): Tikidji-Hamburyan, Ruben [ruben.tikidji.hamburyan at] ;
import time, sys
import numpy as np
import matplotlib
matplotlib.rcParams[""] = ""
from matplotlib import pyplot as plt
from neuron import h



excw,excd =  0.009, 0.8
inhw,inhd = -0.050, 2.1
stmw,stmd =  0.025, 0.5
inspk = np.genfromtxt("../input.ssv")

class liaf:
	def __init__(self):
		self.cell = h.IntFire1()
		self.cell.m = 0.
		self.recorder = h.NetCon(self.cell,None)
		self.spk = h.Vector()

class stim:
	def __init__(self,sid):
		self.s          = h.VecStim()
		self.nvec       = inspk[np.where(inspk[:,1].astype(int) == sid),0][0]
		self.vec        = h.Vector(self.nvec.shape[0])
		self.recorder = h.NetCon(self.s,None)
		self.spk = h.Vector()

E=[ liaf() for x in xrange(Ne) ]
I=[ liaf() for x in xrange(Ni) ]
S=[ stim(x) for x in xrange(Ns) ]

econs,icons,scons =[],[],[]

for pre,post in np.genfromtxt("../ee.ssv").astype(int):
	econs.append( h.NetCon(E[pre].cell,E[post].cell) )

for pre,post in np.genfromtxt("../ei.ssv").astype(int):
	econs.append( h.NetCon(E[pre].cell,I[post].cell) )
for pri,post in np.genfromtxt("../ie.ssv").astype(int):
	icons.append( h.NetCon(I[pri].cell,E[post].cell) )
for pri,post in np.genfromtxt("../ii.ssv").astype(int):
	icons.append( h.NetCon(I[pri].cell,I[post].cell) )

for prs,post in np.genfromtxt("../se.ssv").astype(int):
	scons.append( h.NetCon(S[prs].s  ,E[post].cell) )

sys.stderr.write("====== Network Created! =======\n")

for econ in econs:
	econ.weight[0],econ.delay = excw,excd
for icon in icons:
	icon.weight[0],icon.delay = inhw,inhd
for scon in scons:
	scon.weight[0],scon.delay = stmw,stmd

h.dt = 0.1


while h.t < 1000.:h.fadvance()

endsimulate= time.time()

print("Building time     : %.2f s"%(endbuild-startbuild ))
print("Simulation time   : %.2f s"%(endsimulate-endbuild))
print("Time step         : %.2f ms"%(h.dt))

spkse = np.array( [ (x,ni      ) for ni, nE in enumerate(E) for x in list(np.array(nE.spk)) ] )
spksi = np.array( [ (x,ni+Ne   +100) for ni, nI in enumerate(I) for x in list(np.array(nI.spk)) ] )
spkss = np.array( [ (x,ni+Ne+Ni+200) for ni, nS in enumerate(S) for x in list(np.array(nS.spk)) ] )

spkse = spkse[np.where(spkse[:,0]<200)]
spksi = spksi[np.where(spksi[:,0]<200)]
spkss = spkss[np.where(spkss[:,0]<200)]