def test_stim_pyramidal_impact():
simulation_clock=Clock(dt=.5*ms)
trial_duration=1*second
dcs_start_time=.5*second
stim_levels=[-8,-6,-4,-2,-1,-.5,-.25,0,.25,.5,1,2,4,6,8]
voltages = np.zeros(len(stim_levels))
for idx,stim_level in enumerate(stim_levels):
print('testing stim_level %.3fpA' % stim_level)
eqs = exp_IF(default_params.C, default_params.gL, default_params.EL, default_params.VT, default_params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=default_params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=default_params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=default_params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', default_params.tau1_nmda, default_params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ', Mg=default_params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=default_params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', default_params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=default_params.E_gaba_a)
eqs +=InjectedCurrent('I_dcs: amp')
group=NeuronGroup(1, model=eqs, threshold=-20*mV, refractory=pyr_params.refractory, reset=default_params.Vr,
compile=True, freeze=True, clock=simulation_clock)
group.C=pyr_params.C
group.gL=pyr_params.gL
@network_operation(clock=simulation_clock)
def inject_current(c):
if simulation_clock.t>dcs_start_time:
group.I_dcs=stim_level*pA
monitor=StateMonitor(group, 'vm', simulation_clock, record=True)
net=Network(group, monitor, inject_current)
net.run(trial_duration, report='text')
voltages[idx]=monitor.values[0,-1]*1000
voltages=voltages-voltages[7]
plt.figure()
plt.plot(stim_levels,voltages)
plt.xlabel('Stimulation level (pA)')
plt.ylabel('Voltage Change (mV)')
plt.show()
def test_stim_pyramidal_impact():
simulation_clock = Clock(dt=.5 * ms)
trial_duration = 1 * second
dcs_start_time = .5 * second
stim_levels = [-8, -6, -4, -2, -1, -.5, -.25, 0, .25, .5, 1, 2, 4, 6, 8]
voltages = np.zeros(len(stim_levels))
for idx, stim_level in enumerate(stim_levels):
print('testing stim_level %.3fpA' % stim_level)
eqs = exp_IF(default_params.C, default_params.gL, default_params.EL,
default_params.VT, default_params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp',
E=default_params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp',
E=default_params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', default_params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp',
E=default_params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', default_params.tau1_nmda,
default_params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ',
Mg=default_params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp',
E=default_params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', default_params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp',
E=default_params.E_gaba_a)
eqs += InjectedCurrent('I_dcs: amp')
group = NeuronGroup(1,
model=eqs,
threshold=-20 * mV,
refractory=pyr_params.refractory,
reset=default_params.Vr,
compile=True,
freeze=True,
clock=simulation_clock)
group.C = pyr_params.C
group.gL = pyr_params.gL
@network_operation(clock=simulation_clock)
def inject_current(c):
if simulation_clock.t > dcs_start_time:
group.I_dcs = stim_level * pA
monitor = StateMonitor(group, 'vm', simulation_clock, record=True)
net = Network(group, monitor, inject_current)
net.run(trial_duration, report='text')
voltages[idx] = monitor.values[0, -1] * 1000
voltages = voltages - voltages[7]
plt.figure()
plt.plot(stim_levels, voltages)
plt.xlabel('Stimulation level (pA)')
plt.ylabel('Voltage Change (mV)')
plt.show()
