import moose
from pylab import *
numrepeats = 10 # repeat runtime for numrepeats
frate = 1.0 # pre- and post-synaptic firing rate
# 1 Hz gives ~300s lifetime, ~0.2 efficacy (weight)
# 10 Hz gives ~10s lifetime, ~0.5 efficacy (weight)
# high firing rates make synaptic efficacy go to 0.5.
runtime = 600.0/frate # s
#############################################
# Ca Plasticity parameters: synapses (not for ExcInhNetBase)
#############################################
## Cortical slice values -- Table Suppl 2 in Graupner & Brunel 2012
## Also used in Higgins et al 2014
tauCa = 22.6936e-3 # s # Ca decay time scale
tauSyn = 346.3615 # s # synaptic plasticity time scale
## in vitro values in Higgins et al 2014, faster plasticity
CaPre = 0.56175 # mM
CaPost = 1.2964 # mM
## in vivo values in Higgins et al 2014, slower plasticity
#CaPre = 0.33705 # mM
#CaPost = 0.74378 # mM
delayD = 4.6098e-3 # s # CaPre is added to Ca after this delay
# proxy for rise-time of NMDA
thetaD = 1.0 # mM # depression threshold for Ca
thetaP = 1.3 # mM # potentiation threshold for Ca
gammaD = 331.909 # factor for depression term
gammaP = 725.085 # factor for potentiation term
J = 5e-3 # V # delta function synapse, adds to Vm
weight = 0.43 # initial synaptic weight
# gammaP/(gammaP+gammaD) = eq weight w/o noise
# see eqn (22), noiseSD also appears
# but doesn't work here,
# weights away from 0.4 - 0.5 screw up the STDP rule!!
bistable = True # if bistable is True, use bistable potential for weights
noisy = True # use noisy weight updates given by noiseSD
noiseSD = 3.3501 # if noisy, use noiseSD (3.3501 from Higgins et al 2014)
##########################################
[docs]def main():
"""
Simulate pre and post Poisson firing for a synapse with
Ca plasticity of Graupner and Brunel 2012.
See the trace over time (lifetime) for the synaptic efficacy,
similar to figure 2A of Higgins, Graupner, Brunel, 2014.
Author: Aditya Gilra, NCBS, Bangalore, October, 2014.
"""
prePoisson = moose.RandSpike('/pre')
prePoisson.rate = frate
postPoisson = moose.RandSpike('/post')
postPoisson.rate = frate
syn = moose.GraupnerBrunel2012CaPlasticitySynHandler( '/syn' )
syn.numSynapses = 1 # 1 synapse
# many pre-synaptic inputs can connect to a synapse
# spikes from presynaptic Poisson generator
moose.connect( prePoisson,'spikeOut', syn.synapse[0], 'addSpike')
# post-synaptic spikes from postsynaptic Poisson generator
moose.connect( postPoisson, 'spikeOut', syn, 'addPostSpike')
syn.synapse[0].delay = 0.0
syn.synapse[0].weight = 1.0 # starting weight to decay from
syn.CaInit = 0.0
syn.tauCa = tauCa
syn.tauSyn = tauSyn
syn.CaPre = CaPre
syn.CaPost = CaPost
syn.delayD = delayD
syn.thetaD = thetaD
syn.thetaP = thetaP
syn.gammaD = gammaD
syn.gammaP = gammaP
syn.weightMax = 1.0 # bounds on the weight
syn.weightMin = 0.
syn.noisy = noisy
syn.noiseSD = noiseSD
syn.bistable = bistable
# ###########################################
# Setting up tables
# ###########################################
CaTable = moose.Table( '/plotCa', 1 )
moose.connect( CaTable, 'requestOut', syn, 'getCa')
WtTable = moose.Table( '/plotWeight', 1 )
moose.connect( WtTable, 'requestOut', syn.synapse[0], 'getWeight')
# ###########################################
# Simulate
# ###########################################
dt = 1e-3 # s
# moose simulation
#moose.useClock( 0, '/syn', 'process' )
#moose.useClock( 1, '/pre', 'process' )
#moose.useClock( 1, '/post', 'process' )
# I think MOOSE uses a different clock than 3 for Tables
# anyway, we use the default one and set all clocks to dt below
#moose.useClock( 3, '/plotCa', 'process' )
#moose.useClock( 3, '/plotWeight', 'process' )
## use setClock to set the dt different from default dt
for i in range(10):
moose.setClock( i, dt )
moose.seed(100)
moose.reinit()
# function to make the aPlus and aMinus settle to equilibrium values
print(("Rates of pre- and post-syanptic neurons =",frate))
WtSeries = []
numsteps = int(runtime/dt)
for i in range(numrepeats):
syn.synapse[0].weight = 1.0 # starting weight to decay from
syn.Ca = 0.0
print(("Repeat number",i,"running..."))
moose.start(runtime)
WtSeries.append(WtTable.vector[i*numsteps:(i+1)*numsteps])
WtSeries = array(WtSeries)
WtMean = mean(WtSeries,axis=0)
print("plotting...")
# ###########################################
# Plot the simulated weights and Ca vs time
# ###########################################
timeseries = linspace(0.0,runtime,numsteps)
# Ca plots for the synapse
# only the first repeat
figure(facecolor='w')
plot(timeseries,CaTable.vector[:numsteps],color='r')
plot((timeseries[0],timeseries[-1]),(thetaP,thetaP),color='k',\
linestyle='dashed',label='pot thresh')
plot((timeseries[0],timeseries[-1]),(thetaD,thetaD),color='b',\
linestyle='dashed',label='dep thresh')
legend()
xlabel('time (s)')
ylabel('Ca (arb)')
title("Ca conc in the synapse @ "+str(frate)+" Hz firing")
# Weight plots for the synapse
figure(facecolor='w')
for i in range(numrepeats):
plot(timeseries,WtSeries[i],alpha=0.5)
plot(timeseries,WtMean,color='k',linewidth=2)
xlabel('time (s)')
ylabel('Efficacy')
title("Efficacy of the synapse @ "+str(frate)+" Hz firing")
show()
if __name__ == '__main__':
main()