def _execute(self, x):
#self.G.I = brian.TimedArray(10000 * x * brian.mV, dt=1 * brian.ms)
self.G.I = brian.TimedArray(100 * scipy.dot(x, self.w_in.T) * brian.mV,
dt=1 * brian.ms)
self.network = brian.Network(self.G, self.Mv, self.Ms)
self.network.reinit()
self.network.run((x.shape[0] + 1) * brian.ms)
retval = self.Mv.values[:, 0:x.shape[0]].T
def run_sim(number_neurons=default_number_neurons,
connection_probability=default_connection_probability,
synaptic_weights=default_synaptic_weights,
synaptic_time_constant=default_synaptic_time_constant,
tend=300):
'''run a simulation of a population of leaky integrate-and-fire excitatory neurons that are
randomly connected. The population is injected with a transient current.'''
from brian.units import mvolt, msecond, namp, Mohm
import brian
brian.clear()
El = 0 * mvolt
tau_m = 30 * msecond
tau_syn = synaptic_time_constant * msecond
R = 20 * Mohm
v_threshold = 30 * mvolt
v_reset = 0 * mvolt
tau_refractory = 4 * msecond
eqs = brian.Equations('''
dv/dt = (-(v - El) + R*I)/tau_m : volt
I = I_syn + I_stim : amp
dI_syn/dt = -I_syn/tau_syn : amp
I_stim : amp
''')
external_current = np.zeros(tend)
external_current[np.arange(0, 100)] = 5 * namp
group = brian.NeuronGroup(
model=eqs,
N=number_neurons, threshold=v_threshold, reset=v_reset, refractory=tau_refractory)
group.I_stim = brian.TimedArray(external_current, dt=1*msecond)
connections = brian.Connection(group, group, 'I_syn')
connections.connect_random(sparseness=connection_probability, weight=synaptic_weights*namp)
spike_monitor = brian.SpikeMonitor(group)
population_rate_monitor = brian.PopulationRateMonitor(group, bin=10*msecond)
brian.reinit()
brian.run(tend * msecond)
return spike_monitor, population_rate_monitor
def run_sim(ffExcInputMult=None, ffInhInputMult=None):
"""Run the cond-based LIF neuron simulation. Takes a few minutes to construct network and run
Parameters
----------
ffExcInputMult: scalar: FF input magnitude to E cells. multiply ffInputV by this value and connect to E cells
ffInhInputMult: scalar: FF input magnitude to I cells.
Returns
-------
outDict - spike times, records of continuous values from simulation
"""
# use helper to get input timecourses
(ffInputV, condAddV) = create_input_vectors(
doDebugPlot=False) # multiplied by scalars below
# setup initial state
stT = time.time()
brian.set_global_preferences(usecodegen=True)
brian.set_global_preferences(useweave=True)
brian.set_global_preferences(usecodegenweave=True)
brian.clear(erase=True, all=True)
brian.reinit_default_clock()
clk = brian.Clock(dt=0.05 * ms)
################
# create neurons, define connections
neurNetwork = brian.NeuronGroup(nNet,
model=eqs,
threshold=vthresh,
reset=vrest,
refractory=absRefractoryMs * msecond,
order=1,
compile=True,
freeze=False,
clock=clk)
# create neuron pools
neurCE = neurNetwork.subgroup(nExc)
neurCI = neurNetwork.subgroup(nInh)
connCE = brian.Connection(neurCE, neurNetwork, 'ge')
connCI = brian.Connection(neurCI, neurNetwork, 'gi')
print('n cells: %d, nE,I %d,%d, %s, absRefractoryMs: %d' %
(nNet, nExc, nInh, repr(clk), absRefractoryMs))
# connect the network to itself
connCE.connect_random(neurCE,
neurNetwork,
internalSparseness,
weight=connENetWeight)
connCI.connect_random(neurCI,
neurNetwork,
internalSparseness,
weight=connINetWeight)
# connect inputs that change spont rate
assert (
spontAddRate <= 0
), 'Spont add rate should be negative - convention: neg, excite inhibitory cells'
spontAddNInpSyn = 100
nTotalSpontNeurons = (spontAddNInpSyn * nInh * 0.02)
neurSpont = brian.PoissonGroup(nTotalSpontNeurons,
-1.0 * spontAddRate * Hz)
connCSpont = brian.Connection(neurSpont, neurCI, 'ge')
connCSpont.connect_random(
p=spontAddNInpSyn * 1.0 / nTotalSpontNeurons,
weight=connENetWeight, # match internal excitatory strengths
fixed=True)
# connect the feedforward visual (poisson) inputs to excitatory cells (ff E)
ffExcInputNInpSyn = 100
nTotalFfNeurons = (ffExcInputNInpSyn * ffExcInputNTargs * 0.02
) # one pop of input cells for both E and I FF
_ffExcInputV = ffExcInputMult * np.abs(a_(ffInputV).copy())
assert (np.all(
_ffExcInputV >= 0)), 'Negative FF rates are rectified to zero'
neurFfExcInput = brian.PoissonGroup(
nTotalFfNeurons, lambda t: _ffExcInputV[int(t * 1000)] * Hz)
connCFfExcInput = brian.Connection(neurFfExcInput, neurNetwork, 'ge')
connCFfExcInput.connect_random(neurFfExcInput,
neurCE[0:ffExcInputNTargs],
ffExcInputNInpSyn * 1.0 / nTotalFfNeurons,
weight=connENetWeight,
fixed=True)
# connect the feedforward visual (poisson) inputs to inhibitory cells (ff I)
ffInhInputNInpSyn = 100
_ffInhInputV = ffInhInputMult * np.abs(ffInputV.copy())
assert (np.all(
_ffInhInputV >= 0)), 'Negative FF rates are rectified to zero'
neurFfInhInput = brian.PoissonGroup(
nTotalFfNeurons, lambda t: _ffInhInputV[int(t * 1000)] * Hz)
connCFfInhInput = brian.Connection(neurFfInhInput, neurNetwork, 'ge')
connCFfInhInput.connect_random(
neurFfInhInput,
neurCI[0:ffInhInputNTargs],
ffInhInputNInpSyn * 1.0 / nTotalFfNeurons, # sparseness
weight=connENetWeight,
fixed=True)
# connect added step (ChR2) conductance to excitatory cells
condAddAmp = 4.0
gAdd = brian.TimedArray(condAddAmp * condAddV, dt=1 * ms)
print('Adding conductance for %d cells (can be slow): ' %
len(condAddNeurNs),
end=' ')
for (iN, tN) in enumerate(condAddNeurNs):
neurCE[tN].gAdd = gAdd
print('done')
# Initialize using some randomness so all neurons don't start in same state.
# Alternative: initialize with constant values, give net extra 100-300ms to evolve from initial state.
neurNetwork.v = (brian.randn(1) * 5.0 - 65) * mvolt
neurNetwork.ge = brian.randn(nNet) * 1.5 + 4
neurNetwork.gi = brian.randn(nNet) * 12 + 20
# Record continuous variables and spikes
monSTarg = brian.SpikeMonitor(neurNetwork)
if contRecNs is not None:
contRecClock = brian.Clock(dt=contRecStepMs * ms)
monVTarg = brian.StateMonitor(neurNetwork,
'v',
record=contRecNs,
clock=contRecClock)
monGETarg = brian.StateMonitor(neurNetwork,
'ge',
record=contRecNs,
clock=contRecClock)
monGAddTarg = brian.StateMonitor(neurNetwork,
'gAdd',
record=contRecNs,
clock=contRecClock)
monGITarg = brian.StateMonitor(neurNetwork,
'gi',
record=contRecNs,
clock=contRecClock)
# construct brian.Network before running (so brian explicitly knows what to update during run)
netL = [
neurNetwork, connCE, connCI, monSTarg, neurFfExcInput, connCFfExcInput,
neurFfInhInput, connCFfInhInput, neurSpont, connCSpont
]
if contRecNs is not None:
# noinspection PyUnboundLocalVariable
netL.append([monVTarg, monGETarg, monGAddTarg,
monGITarg]) # cont monitors
net = brian.Network(netL)
print("Network construction time: %3.1f seconds" % (time.time() - stT))
# run
print("Simulation running...")
sys.stdout.flush()
start_time = time.time()
net.run(simRunTimeS * second, report='text', report_period=30.0 * second)
durationS = time.time() - start_time
print("Simulation time: %3.1f seconds" % durationS)
outNTC = collections.namedtuple(
'outNTC',
'vm ge gadd gi clockDtS clockStartS clockEndS spiketimes contRecNs')
outNTC.__new__.__defaults__ = (None, ) * len(
outNTC._fields) # default to None
outNT = outNTC(clockDtS=float(monSTarg.clock.dt),
clockStartS=float(monSTarg.clock.start),
clockEndS=float(monSTarg.clock.end),
spiketimes=a_(monSTarg.spiketimes.values(), dtype='O'),
contRecNs=contRecNs)
if contRecNs is not None:
outNT = outNT._replace(vm=monVTarg.values,
ge=monGETarg.values,
gadd=monGAddTarg.values,
gi=monGITarg.values)
return outNT
def fft_std(delta_u, run_num, new_connectivity, osc, rep):
#bn.seed(int(time.time()))
bn.reinit_default_clock()
#bn.seed(1412958308+2)
bn.defaultclock.dt = 0.5 * bn.ms
#==============================================================================
# Define constants for the model.
#==============================================================================
fft_file = './std_fft_p20_'
rate_file = './std_rate_p20_'
print delta_u
print run_num
print new_connectivity
print rep
if osc:
T = 5.5 * bn.second
else:
T = 2.5 * bn.second
n_tsteps = T / bn.defaultclock.dt
fft_start = 0.5 * bn.second / bn.defaultclock.dt # Time window for the FFT computation
ro = 1.2 * bn.Hz
SEE1 = 1.0
SEE2 = 1.0
qee1 = 1.00 # Fraction of NMDA receptors for e to e connections
qee2 = 0.00
qie1 = 1.00 # Fraction of NMDA receptors for e to i connections
qie2 = 0.00
uee1 = 0.2 - delta_u
uee2 = 0.2 + delta_u
uie1 = 0.2
uie2 = 0.2
trec1 = 1000.0 * bn.ms
trec2 = 1000.0 * bn.ms
k = 0.65
#Jeo_const = 1.0#*bn.mV # Base strength of o (external) to e connections
Ne = 3200 # number of excitatory neurons
Ni = 800 # number of inhibitory neurons
No = 20000 # number of external neurons
N = Ne + Ni
pcon = 0.2 # probability of connection
Jee = 10.0 / (Ne * pcon)
Jie = 10.0 / (Ne * pcon)
Jii = k * 10.0 / (Ni * pcon)
Jei = k * 10.0 / (Ni * pcon)
Jeo = 1.0
El = -60.0 * bn.mV # leak reversal potential
Vreset = -52.0 * bn.mV # reversal potential
Vthresh = -40.0 * bn.mV # spiking threshold
tref = 2.0 * bn.ms # refractory period
te = 20.0 * bn.ms # membrane time constant of excitatory neurons
ti = 10.0 * bn.ms # membrane time constant of inhibitory neruons
tee_ampa = 10.0 * bn.ms # time const of ampa currents at excitatory neurons
tee_nmda = 100.0 * bn.ms # time const of nmda currents at excitatory neurons
tie_ampa = 10.0 * bn.ms # time const of ampa currents at inhibitory neurons
tie_nmda = 100.0 * bn.ms # time const of nmda currents at inhibitory neurons
tii_gaba = 10.0 * bn.ms # time const of GABA currents at inhibitory neurons
tei_gaba = 10.0 * bn.ms # time const of GABA currents at excitatory neurons
teo_input = 100.0 * bn.ms
#==============================================================================
# Define model structure
#==============================================================================
model = '''
dV/dt = (-(V-El)+J_ampa1*I_ampa1+J_nmda1*I_nmda1+J_ampa2*I_ampa2+J_nmda2*I_nmda2-J_gaba*I_gaba+J_input*I_input+eta)/tm : bn.volt
dI_ampa1/dt = -I_ampa1/t_ampa : bn.volt
dI_nmda1/dt = -I_nmda1/t_nmda : bn.volt
dI_ampa2/dt = -I_ampa2/t_ampa : bn.volt
dI_nmda2/dt = -I_nmda2/t_nmda : bn.volt
dI_gaba/dt = -I_gaba/t_gaba : bn.volt
dI_input/dt = (-I_input+mu)/t_input : bn.volt
dx1/dt = (1-x1)/t1_rec : 1
dx2/dt = (1-x2)/t2_rec : 1
u1 : 1
t1_rec : bn.second
u2 : 1
t2_rec : bn.second
mu : bn.volt
eta : bn.volt
J_ampa1 : 1
J_nmda1 : 1
J_ampa2 : 1
J_nmda2 : 1
J_gaba : 1
J_input : 1
tm : bn.second
t_ampa : bn.second
t_nmda : bn.second
t_gaba : bn.second
t_input : bn.second
'''
P_reset = "V=-52*bn.mV;x1+=-u1*x1;x2+=-u2*x2"
Se_model = '''
we_ampa1 : bn.volt
we_nmda1 : bn.volt
we_ampa2 : bn.volt
we_nmda2 : bn.volt
'''
Se_pre = ('I_ampa1 += x1_pre*we_ampa1', 'I_nmda1 += x1_pre*we_nmda1',
'I_ampa2 += x2_pre*we_ampa2', 'I_nmda2 += x2_pre*we_nmda2')
Si_model = '''
wi_gaba : bn.volt
'''
Si_pre = 'I_gaba += wi_gaba'
So_model = '''
wo_input : bn.volt
'''
So_pre = 'I_input += wo_input'
#==============================================================================
# Define populations
#==============================================================================
P = bn.NeuronGroup(N,
model,
threshold=Vthresh,
reset=P_reset,
refractory=tref)
Pe = P[0:Ne]
Pe.tm = te
Pe.t_ampa = tee_ampa
Pe.t_nmda = tee_nmda
Pe.t_gaba = tei_gaba
Pe.t_input = teo_input
Pe.I_ampa1 = 0 * bn.mV
Pe.I_nmda1 = 0 * bn.mV
Pe.I_ampa2 = 0 * bn.mV
Pe.I_nmda2 = 0 * bn.mV
Pe.I_gaba = 0 * bn.mV
Pe.I_input = 0 * bn.mV
Pe.V = (np.random.rand(Pe.V.size) * 12 - 52) * bn.mV
Pe.x1 = 1.0
Pe.x2 = 1.0
Pe.u1 = uee1
Pe.u2 = uee2
Pe.t1_rec = trec1
Pe.t2_rec = trec2
Pi = P[Ne:(Ne + Ni)]
Pi.tm = ti
Pi.t_ampa = tie_ampa
Pi.t_nmda = tie_nmda
Pi.t_gaba = tii_gaba
Pi.t_input = teo_input
Pi.I_ampa1 = 0 * bn.mV
Pi.I_nmda1 = 0 * bn.mV
Pi.I_ampa2 = 0 * bn.mV
Pi.I_nmda2 = 0 * bn.mV
Pi.I_gaba = 0 * bn.mV
Pi.I_input = 0 * bn.mV
Pi.V = (np.random.rand(Pi.V.size) * 12 - 52) * bn.mV
Pi.x1 = 1.0
Pi.x2 = 1.0
Pi.u1 = 0.0
Pi.u2 = 0.0
Pi.t1_rec = 1.0
Pi.t2_rec = 1.0
Pe.J_ampa1 = Jee * (1 - qee1) #*SEE1
Pe.J_nmda1 = Jee * qee1 #*SEE1
Pe.J_ampa2 = Jee * (1 - qee2) #*SEE2
Pe.J_nmda2 = Jee * qee2 #*SEE2
Pi.J_ampa1 = Jie * (1 - qie2) #*SEE2
Pi.J_nmda1 = Jie * qie2 #*SEE2
Pi.J_ampa2 = Jie * (1 - qie1) #*SEE1
Pi.J_nmda2 = Jie * qie1 #*SEE1
Pe.J_gaba = Jei
Pi.J_gaba = Jii
Pe.J_input = Jeo
Pi.J_input = Jeo
#==============================================================================
# Define inputs
#==============================================================================
if osc:
Pe.mu = 12.0 * bn.mV
holder = np.zeros((n_tsteps, ))
t_freq = np.linspace(0, 10, n_tsteps)
fo = 0.2 # Smallest frequency in the signal
fe = 10.0 # Largest frequency in the signal
F = int(fe / 0.2)
for m in range(1, F + 1):
holder = holder + np.cos(2 * np.pi * m * fo * t_freq - m *
(m - 1) * np.pi / F)
holder = holder / np.max(holder)
Pe.eta = bn.TimedArray(0.0 * bn.mV * holder) #, dt=0.5*bn.ms)
Background_eo = bn.PoissonInput(Pe,
N=1000,
rate=1.05 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
Background_io = bn.PoissonInput(Pi,
N=1000,
rate=1.0 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
Pi.mu = 0 * bn.mV
Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms)
Po = bn.PoissonGroup(No, rates=0 * bn.Hz)
else:
Background_eo = bn.PoissonInput(Pe,
N=1000,
rate=1.05 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
Background_io = bn.PoissonInput(Pi,
N=1000,
rate=1.0 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
holder_pe = np.zeros((n_tsteps, ))
time_steps = np.linspace(0, T / bn.second, n_tsteps)
holder_pe[time_steps < 0.5] = 0.0 * bn.mV
holder_pe[time_steps >= 0.5] = 6.0 * bn.mV #25
holder_pe[time_steps > 1.5] = 0.0 * bn.mV #25
Pe.mu = bn.TimedArray(holder_pe)
def firing_function(t, ro):
if t > 0.5 * bn.second and t < 3.5 * bn.second:
return 0.0 * bn.Hz
else:
return 0.0 * bn.Hz
Pe.eta = 0 * bn.mV #, dt=0.5*bn.ms)
Pi.mu = 0.0 * bn.mV
Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms)
Po = bn.PoissonGroup(No, rates=lambda t: firing_function(t, ro))
#==============================================================================
# Define synapses
#==============================================================================
See1 = bn.Synapses(Pe, Pe, model=Se_model, pre=Se_pre)
See2 = bn.Synapses(Pe, Pe, model=Se_model, pre=Se_pre)
Sie1 = bn.Synapses(Pe, Pi, model=Se_model, pre=Se_pre)
Sie2 = bn.Synapses(Pe, Pi, model=Se_model, pre=Se_pre)
Sei = bn.Synapses(Pi, Pe, model=Si_model, pre=Si_pre)
Sii = bn.Synapses(Pi, Pi, model=Si_model, pre=Si_pre)
Seo = bn.Synapses(Po, Pe, model=So_model, pre=So_pre)
#==============================================================================
# Define random connections
#==============================================================================
if new_connectivity:
See1.connect_random(Pe, Pe, sparseness=pcon / 2.0)
See2.connect_random(Pe, Pe, sparseness=pcon / 2.0)
Sie1.connect_random(Pe, Pi, sparseness=pcon / 2.0)
Sie2.connect_random(Pe, Pi, sparseness=pcon / 2.0)
Sii.connect_random(Pi, Pi, sparseness=pcon)
Sei.connect_random(Pi, Pe, sparseness=pcon)
Seo.connect_random(Po, Pe, sparseness=pcon)
print 'Saving'
See1.save_connectivity('./See1_connections_std_saver_p20_' +
str(run_num))
See2.save_connectivity('./See2_connections_std_saver_p20_' +
str(run_num))
Sie1.save_connectivity('./Sie1_connections_std_saver_p20_' +
str(run_num))
Sie2.save_connectivity('./Sie2_connections_std_saver_p20_' +
str(run_num))
Sii.save_connectivity('./Sii_connections_std_saver_p20_' +
str(run_num))
Sei.save_connectivity('./Sei_connections_std_saver_p20_' +
str(run_num))
Seo.save_connectivity('./Seo_connections_std_saver_p20_' +
str(run_num))
else:
print 'Loading'
See1.load_connectivity('./See1_connections_std_saver_p20_' +
str(run_num))
See2.load_connectivity('./See2_connections_std_saver_p20_' +
str(run_num))
Sie1.load_connectivity('./Sie1_connections_std_saver_p20_' +
str(run_num))
Sie2.load_connectivity('./Sie2_connections_std_saver_p20_' +
str(run_num))
Sii.load_connectivity('./Sii_connections_std_saver_p20_' +
str(run_num))
Sei.load_connectivity('./Sei_connections_std_saver_p20_' +
str(run_num))
Seo.load_connectivity('./Seo_connections_std_saver_p20_' +
str(run_num))
See1.we_ampa1 = SEE1 * 1.0 * bn.mV / tee_ampa
See1.we_nmda1 = SEE1 * 1.0 * bn.mV / tee_nmda
See1.we_ampa2 = 0.0 * bn.mV / tee_ampa
See1.we_nmda2 = 0.0 * bn.mV / tee_nmda
See2.we_ampa1 = 0.0 * bn.mV / tee_ampa
See2.we_nmda1 = 0.0 * bn.mV / tee_nmda
See2.we_ampa2 = SEE2 * 1.0 * bn.mV / tee_ampa
See2.we_nmda2 = SEE2 * 1.0 * bn.mV / tee_nmda
Sie1.we_ampa1 = 0.0 * bn.mV / tie_ampa
Sie1.we_nmda1 = 0.0 * bn.mV / tie_nmda
Sie1.we_ampa2 = SEE1 * 1.0 * bn.mV / tie_ampa
Sie1.we_nmda2 = SEE1 * 1.0 * bn.mV / tie_nmda
Sie2.we_ampa1 = SEE2 * 1.0 * bn.mV / tie_ampa
Sie2.we_nmda1 = SEE2 * 1.0 * bn.mV / tie_nmda
Sie2.we_ampa2 = 0.0 * bn.mV / tie_ampa
Sie2.we_nmda2 = 0.0 * bn.mV / tie_nmda
Sei.wi_gaba = 1.0 * bn.mV / tei_gaba
Sii.wi_gaba = 1.0 * bn.mV / tii_gaba
Seo.wo_input = 1.0 * bn.mV / teo_input
#==============================================================================
# Define monitors
#==============================================================================
Pe_mon_V = bn.StateMonitor(Pe, 'V', timestep=10, record=True)
Pe_mon_eta = bn.StateMonitor(Pe, 'eta', timestep=1, record=True)
Pe_mon_ampa1 = bn.StateMonitor(Pe, 'I_ampa1', timestep=1, record=True)
Pe_mon_nmda1 = bn.StateMonitor(Pe, 'I_nmda1', timestep=1, record=True)
Pe_mon_ampa2 = bn.StateMonitor(Pe, 'I_ampa2', timestep=1, record=True)
Pe_mon_nmda2 = bn.StateMonitor(Pe, 'I_nmda2', timestep=1, record=True)
Pe_mon_gaba = bn.StateMonitor(Pe, 'I_gaba', timestep=1, record=True)
Pe_mon_input = bn.StateMonitor(Pe, 'I_input', timestep=10, record=True)
See1_mon_x = bn.StateMonitor(Pe, 'x1', timestep=10, record=True)
See2_mon_x = bn.StateMonitor(Pe, 'x2', timestep=10, record=True)
Pe_ratemon = bn.PopulationRateMonitor(Pe, bin=10.0 * bn.ms)
Pi_ratemon = bn.PopulationRateMonitor(Pi, bin=10.0 * bn.ms)
#==============================================================================
# Run model
#==============================================================================
timer = 0 * bn.second
t_start = time.time()
bn.run(T, report='graphical')
timer = timer + T
print '-------------------------------------------------------'
print 'Time is ' + str(timer) + ' seconds'
t_end = time.time()
print 'Time to compute last ' +str(T)+' seconds is: ' + \
str(t_end - t_start) + ' seconds'
print '-------------------------------------------------------\n'
Pe_mon_ampa1_vals = Pe.J_ampa1[0] * np.mean(Pe_mon_ampa1.values.T, axis=1)
Pe_mon_nmda1_vals = Pe.J_nmda1[0] * np.mean(Pe_mon_nmda1.values.T, axis=1)
Pe_mon_ampa2_vals = Pe.J_ampa2[0] * np.mean(Pe_mon_ampa2.values.T, axis=1)
Pe_mon_nmda2_vals = Pe.J_nmda2[0] * np.mean(Pe_mon_nmda2.values.T, axis=1)
Pe_mon_ampa_vals = Pe_mon_ampa1_vals + Pe_mon_ampa2_vals
Pe_mon_nmda_vals = Pe_mon_nmda1_vals + Pe_mon_nmda2_vals
Pe_mon_gaba_vals = Pe.J_gaba[0] * np.mean(Pe_mon_gaba.values.T, axis=1)
Pe_mon_input_vals = Pe.J_input[0] * np.mean(Pe_mon_input.values.T, axis=1)
Pe_mon_V_vals = np.mean(Pe_mon_V.values.T, axis=1)
Pe_mon_all_vals = Pe_mon_ampa_vals + Pe_mon_nmda_vals - Pe_mon_gaba_vals
See1_mon_x_vals = np.mean(See1_mon_x.values.T, axis=1)
See2_mon_x_vals = np.mean(See2_mon_x.values.T, axis=1)
#==============================================================================
# Save into a Matlab file
#==============================================================================
if osc:
Pe_output = Pe.J_ampa1[0]*Pe_mon_ampa1.values+Pe.J_nmda1[0]*Pe_mon_nmda1.values + \
Pe.J_ampa2[0]*Pe_mon_ampa2.values+Pe.J_nmda2[0]*Pe_mon_nmda2.values-Pe.J_gaba[0]*Pe_mon_gaba.values
Pe_output = Pe_output[:, fft_start:, ]
Pe_V = Pe_mon_V.values[:, fft_start:, ]
Pe_glut = Pe.J_ampa1[0]*Pe_mon_ampa1.values+Pe.J_nmda1[0]*Pe_mon_nmda1.values + \
Pe.J_ampa2[0]*Pe_mon_ampa2.values+Pe.J_nmda2[0]*Pe_mon_nmda2.values
Pe_glut = Pe_glut[:, fft_start:, ]
Pe_gaba = Pe.J_gaba[0] * Pe_mon_gaba.values
Pe_gaba = Pe_gaba[:, fft_start:, ]
Pe_input = Pe_mon_eta[:, fft_start:, ]
T_step = bn.defaultclock.dt
holder = {
'Pe_output': Pe_output,
'Pe_input': Pe_input,
'Pe_V': Pe_V,
'Pe_glut': Pe_glut,
'Pe_gaba': Pe_gaba,
'T_step': T_step
}
scipy.io.savemat(fft_file + 'delta_u' + str(delta_u) + '_' + str(rep),
mdict=holder)
else:
holder = {
'Pe_rate': Pe_ratemon.rate,
'Pe_time': Pe_ratemon.times,
'uee1': uee1,
'uee2': uee2,
'uie1': uie1,
'uie2': uie2
}
scipy.io.savemat(rate_file + 'delta_q_' + str(delta_u) + '_' +
str(run_num) + 'rep' + str(rep),
mdict=holder)
bn.clear(erase=True, all=True)
def fft_nostd(qee, run_num, new_connectivity, osc, rep):
#bn.seed(int(time.time()))
# bn.seed(1412958308+2)
bn.reinit_default_clock()
bn.defaultclock.dt = 0.5 * bn.ms
#==============================================================================
# Define constants for the model.
#==============================================================================
fft_file = './nostd_fft_p20_'
rate_file = './nostd_rate_p20_'
if osc:
T = 8.0 * bn.second
else:
T = 3.5 * bn.second
n_tsteps = T / bn.defaultclock.dt
fft_start = 3.0 * bn.second / bn.defaultclock.dt # Time window for the FFT computation
#run_num = 10
ro = 1.2 * bn.Hz
#==============================================================================
# Need to do all others besides 0.2 and 0.5
#==============================================================================
print qee
print run_num
print new_connectivity
print rep
qie = 0.3 # Fraction of NMDA receptors for e to i connections
k = 0.65
Jeo_const = 1.0 #*bn.mV # Base strength of o (external) to e connections
Ne = 3200 # number of excitatory neurons
Ni = 800 # number of inhibitory neurons
No = 2000 # number of external neurons
N = Ne + Ni
pcon = 0.2 # probability of connection
Jee = 5.0 / (Ne * pcon)
Jie = 5.0 / (Ne * pcon)
Jii = k * 5.0 / (Ni * pcon)
Jei = k * 5.0 / (Ni * pcon)
Jeo = 1.0
El = -60.0 * bn.mV # leak reversal potential
Vreset = -52.0 * bn.mV # reversal potential
Vthresh = -40.0 * bn.mV # spiking threshold
tref = 2.0 * bn.ms # refractory period
te = 20.0 * bn.ms # membrane time constant of excitatory neurons
ti = 10.0 * bn.ms # membrane time constant of inhibitory neruons
tee_ampa = 10.0 * bn.ms # time const of ampa currents at excitatory neurons
tee_nmda = 100.0 * bn.ms # time const of nmda currents at excitatory neurons
tie_ampa = 10.0 * bn.ms # time const of ampa currents at inhibitory neurons
tie_nmda = 100.0 * bn.ms # time const of nmda currents at inhibitory neurons
tii_gaba = 10.0 * bn.ms # time const of GABA currents at inhibitory neurons
tei_gaba = 10.0 * bn.ms # time const of GABA currents at excitatory neurons
teo_input = 100.0 * bn.ms
#==============================================================================
# Define model structure
#==============================================================================
model = '''
dV/dt = (-(V-El)+J_ampa*I_ampa+J_nmda*I_nmda-J_gaba*I_gaba+J_input*I_input+eta+eta_corr)/tm : bn.volt
dI_ampa/dt = -I_ampa/t_ampa : bn.volt
dI_nmda/dt = -I_nmda/t_nmda : bn.volt
dI_gaba/dt = -I_gaba/t_gaba : bn.volt
dI_input/dt = (-I_input+mu)/t_input : bn.volt
J_ampa : 1
J_nmda : 1
J_gaba : 1
J_input : 1
mu : bn.volt
eta : bn.volt
eta_corr : bn.volt
tm : bn.second
t_ampa : bn.second
t_nmda : bn.second
t_gaba : bn.second
t_input : bn.second
'''
P_reset = "V=-52*bn.mV"
Se_model = '''
we_ampa : bn.volt
we_nmda : bn.volt
'''
Se_pre = ('I_ampa += we_ampa', 'I_nmda += we_nmda')
Si_model = '''
wi_gaba : bn.volt
'''
Si_pre = 'I_gaba += wi_gaba'
So_model = '''
wo_input : bn.volt
'''
So_pre = 'I_input += wo_input'
#==============================================================================
# Define populations
#==============================================================================
P = bn.NeuronGroup(N,
model,
threshold=Vthresh,
reset=P_reset,
refractory=tref)
Pe = P[0:Ne]
Pe.tm = te
Pe.t_ampa = tee_ampa
Pe.t_nmda = tee_nmda
Pe.t_gaba = tei_gaba
Pe.t_input = teo_input
Pe.I_ampa = 0 * bn.mV
Pe.I_nmda = 0 * bn.mV
Pe.I_gaba = 0 * bn.mV
Pe.I_input = 0 * bn.mV
Pe.V = (np.random.rand(Pe.V.size) * 12 - 52) * bn.mV
Pi = P[Ne:(Ne + Ni)]
Pi.tm = ti
Pi.t_ampa = tie_ampa
Pi.t_nmda = tie_nmda
Pi.t_gaba = tii_gaba
Pi.t_input = teo_input
Pi.I_ampa = 0 * bn.mV
Pi.I_nmda = 0 * bn.mV
Pi.I_gaba = 0 * bn.mV
Pi.I_input = 0 * bn.mV
Pi.V = (np.random.rand(Pi.V.size) * 12 - 52) * bn.mV
Pe.J_ampa = Jee * (1 - qee) #*SEE1
Pe.J_nmda = Jee * qee #*SEE1
Pi.J_ampa = Jie * (1 - qie) #*SEE1
Pi.J_nmda = Jie * qie #*SEE1
Pe.J_gaba = Jei
Pi.J_gaba = Jii
Pe.J_input = Jeo
Pi.J_input = Jeo
#==============================================================================
# Define inputs
#==============================================================================
if osc:
Pe.mu = 2.0 * bn.mV
holder = np.zeros((n_tsteps, ))
t_freq = np.linspace(0, 10, n_tsteps)
fo = 0.2 # Smallest frequency in the signal
fe = 10.0 # Largest frequency in the signal
F = int(fe / 0.2)
for m in range(1, F + 1):
holder = holder + np.cos(2 * np.pi * m * fo * t_freq - m *
(m - 1) * np.pi / F)
holder = holder / np.max(holder)
Pe.eta = bn.TimedArray(0.0 * bn.mV * holder) #, dt=0.5*bn.ms)
Pe.eta_corr = 0 * bn.mV
Background_eo = bn.PoissonInput(Pe,
N=1000,
rate=1.0 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
Background_io = bn.PoissonInput(Pi,
N=1000,
rate=1.05 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
Pi.mu = 0 * bn.mV
Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms)
Pi.eta_corr = 0 * bn.mV
Po = bn.PoissonGroup(No, rates=0 * bn.Hz)
else:
Background_eo = bn.PoissonInput(Pe,
N=1000,
rate=1.0 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
Background_io = bn.PoissonInput(Pi,
N=1000,
rate=1.05 * bn.Hz,
weight=0.2 * bn.mV,
state='I_input')
holder_pe = np.zeros((n_tsteps, ))
time_steps = np.linspace(0, T / bn.second, n_tsteps)
holder_pe[time_steps < 0.5] = 0.0 * bn.mV
holder_pe[time_steps >= 0.5] = 3.0 * bn.mV # 35.0/Jeo *bn.mV #25
Pe.mu = bn.TimedArray(holder_pe)
def firing_function(t, ro):
if t > 0.5 * bn.second and t < 3.5 * bn.second:
return 0.0 * bn.Hz
else:
return 0.0 * bn.Hz
# Pe.mu = 0*bn.mV
Pe.eta = 0 * bn.mV #, dt=0.5*bn.ms)
Pi.mu = 0.0 * bn.mV
Pi.eta = 0 * bn.mV #, dt=0.5*bn.ms)
Po = bn.PoissonGroup(No, rates=lambda t: firing_function(t, ro))
#==============================================================================
# Define synapses
#==============================================================================
See = bn.Synapses(Pe, Pe, model=Se_model, pre=Se_pre)
Sie = bn.Synapses(Pe, Pi, model=Se_model, pre=Se_pre)
Sei = bn.Synapses(Pi, Pe, model=Si_model, pre=Si_pre)
Sii = bn.Synapses(Pi, Pi, model=Si_model, pre=Si_pre)
Seo = bn.Synapses(Po, Pe, model=So_model, pre=So_pre)
#==============================================================================
# Define monitors
#==============================================================================
Pe_mon_V = bn.StateMonitor(Pe, 'V', timestep=1, record=True)
Pe_mon_eta = bn.StateMonitor(Pe, 'eta', timestep=1, record=True)
Pe_mon_ampa = bn.StateMonitor(Pe, 'I_ampa', timestep=1, record=True)
Pe_mon_nmda = bn.StateMonitor(Pe, 'I_nmda', timestep=1, record=True)
Pe_mon_gaba = bn.StateMonitor(Pe, 'I_gaba', timestep=1, record=True)
Pe_ratemon = bn.PopulationRateMonitor(Pe, bin=10.0 * bn.ms)
#==============================================================================
# Define random connections
#==============================================================================
if new_connectivity:
See.connect_random(Pe, Pe, sparseness=pcon)
Sie.connect_random(Pe, Pi, sparseness=pcon)
Sii.connect_random(Pi, Pi, sparseness=pcon)
Sei.connect_random(Pi, Pe, sparseness=pcon)
Seo.connect_random(Po, Pe, sparseness=pcon)
print 'Saving'
See.save_connectivity('./See_connections_nostd_saver_p20' +
str(run_num))
Sie.save_connectivity('./Sie_connections_nostd_saver_p20' +
str(run_num))
Sii.save_connectivity('./Sii_connections_nostd_saver_p20' +
str(run_num))
Sei.save_connectivity('./Sei_connections_nostd_saver_p20' +
str(run_num))
Seo.save_connectivity('./Seo_connections_nostd_saver_p20' +
str(run_num))
else:
print 'Loading'
See.load_connectivity('./See_connections_nostd_saver_p20' +
str(run_num))
Sie.load_connectivity('./Sie_connections_nostd_saver_p20' +
str(run_num))
Sii.load_connectivity('./Sii_connections_nostd_saver_p20' +
str(run_num))
Sei.load_connectivity('./Sei_connections_nostd_saver_p20' +
str(run_num))
Seo.load_connectivity('./Seo_connections_nostd_saver_p20' +
str(run_num))
See.we_ampa = 1.0 * bn.mV / tee_ampa
See.we_nmda = 1.0 * bn.mV / tee_nmda
Sie.we_ampa = 1.0 * bn.mV / tie_ampa
Sie.we_nmda = 1.0 * bn.mV / tie_nmda
Sei.wi_gaba = 1.0 * bn.mV / tei_gaba
Sii.wi_gaba = 1.0 * bn.mV / tii_gaba
Seo.wo_input = 1.0 * bn.mV / teo_input
#==============================================================================
# Run model
#==============================================================================
timer = 0 * bn.second
t_start = time.time()
bn.run(T, report='graphical')
timer = timer + T
print '-------------------------------------------------------'
print 'Time is ' + str(timer) + ' seconds'
t_end = time.time()
print 'Time to compute last ' +str(T)+' seconds is: ' + \
str(t_end - t_start) + ' seconds'
print '-------------------------------------------------------\n'
#==============================================================================
# Save into a Matlab file
#==============================================================================
if osc:
Pe_output = Pe.J_ampa[0] * Pe_mon_ampa.values + Pe.J_nmda[
0] * Pe_mon_nmda.values - Pe.J_gaba[0] * Pe_mon_gaba.values
Pe_output = Pe_output[:, fft_start:, ]
Pe_glut = Pe.J_ampa[0] * Pe_mon_ampa.values + Pe.J_nmda[
0] * Pe_mon_nmda.values
Pe_glut = Pe_glut[:, fft_start:, ]
Pe_gaba = Pe.J_gaba[0] * Pe_mon_gaba.values[:, fft_start:, ]
Pe_V = Pe_mon_V.values[:, fft_start:, ]
Pe_input = Pe_mon_eta[:, fft_start:, ]
T_step = bn.defaultclock.dt
holder = {
'Pe_output': Pe_output,
'Pe_input': Pe_input,
'Pe_V': Pe_V,
'Pe_glut': Pe_glut,
'Pe_gaba': Pe_gaba,
'T_step': T_step
}
scipy.io.savemat(fft_file + 'qee' + str(qee) + '_' + str(rep),
mdict=holder)
else:
holder = {'Pe_rate': Pe_ratemon.rate, 'Pe_time': Pe_ratemon.times}
scipy.io.savemat(rate_file + 'qee_' + str(qee) + '_' + str(run_num) +
'rep' + str(rep),
mdict=holder)