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 __init__(self, params=default_params, pyr_params=pyr_params(), inh_params=inh_params(),
background_input=None, task_inputs=None, clock=defaultclock):
self.params=params
self.pyr_params=pyr_params
self.inh_params=inh_params
self.background_input=background_input
self.task_inputs=task_inputs
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
eqs += Equations('g_muscimol : nS')
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ', Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
eqs +=InjectedCurrent('I_dcs: amp')
NeuronGroup.__init__(self, params.network_group_size, model=eqs, threshold=-20*mV, refractory=1*ms,
reset=params.Vr, compile=True, freeze=True, clock=clock)
self.init_subpopulations()
self.init_connectivity(clock)
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 __init__(self,
params=default_params,
pyr_params=pyr_params(),
inh_params=inh_params(),
plasticity_params=plasticity_params(),
background_input=None,
task_inputs=None,
clock=defaultclock):
self.params = params
self.pyr_params = pyr_params
self.inh_params = inh_params
self.plasticity_params = plasticity_params
self.background_input = background_input
self.task_inputs = task_inputs
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
eqs += Equations('g_muscimol : nS')
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda,
siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ',
Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=(g_gaba_a+g_muscimol)*(E-vm): amp',
E=params.E_gaba_a)
eqs += InjectedCurrent('I_dcs: amp')
# Total synaptic conductance
eqs += Equations(
'g_syn=g_ampa_r+g_ampa_x+g_ampa_b+g_V*g_nmda+g_gaba_a : siemens')
eqs += Equations(
'g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_b+g_V*g_nmda : siemens')
# Total synaptic current
eqs += Equations(
'I_abs=(I_ampa_r**2)**.5+(I_ampa_b**2)**.5+(I_ampa_x**2)**.5+(I_nmda**2)**.5+(I_gaba_a**2)**.5 : amp'
)
NeuronGroup.__init__(self,
params.network_group_size,
model=eqs,
threshold=-20 * mV,
refractory=1 * ms,
reset=params.Vr,
compile=True,
freeze=True,
clock=clock)
self.init_subpopulations()
self.init_connectivity(clock)
def __init__(self,
lip_size,
params,
background_inputs=None,
visual_cortex_input=None,
go_input=None):
self.lip_size = lip_size
self.N = 2 * self.lip_size
self.params = params
self.background_inputs = background_inputs
self.visual_cortex_input = visual_cortex_input
self.go_input = go_input
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - go input current
eqs += exp_synapse('g_ampa_g', params.tau_ampa, siemens)
eqs += Current('I_ampa_g=g_ampa_g*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda,
siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ',
Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
# GABA-B conductance
eqs += biexp_synapse('g_gaba_b', params.tau1_gaba_b,
params.tau2_gaba_b, siemens)
eqs += Current('I_gaba_b=g_gaba_b*(E-vm): amp', E=params.E_gaba_b)
# Total synaptic conductance
eqs += Equations(
'g_syn=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda+g_gaba_a+g_gaba_b : siemens'
)
eqs += Equations(
'g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda : siemens'
)
# Total synaptic current
eqs += Equations(
'I_abs=abs(I_ampa_r)+abs(I_ampa_b)+abs(I_ampa_x)+abs(I_ampa_g)+abs(I_nmda)+abs(I_gaba_a) : amp'
)
NeuronGroup.__init__(self,
self.N,
model=eqs,
threshold=-20 * mV,
reset=params.EL,
compile=True)
self.init_subpopulations()
self.connections = []
self.init_connectivity()
if self.background_inputs is not None:
# Background -> E+I population connections
background_left_ampa = init_connection(self.background_inputs[0],
self.left_lip.neuron_group,
'g_ampa_b',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_b_e,
delay=5 * ms)
background_right_ampa = init_connection(
self.background_inputs[1],
self.right_lip.neuron_group,
'g_ampa_b',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_b_e,
delay=5 * ms)
self.connections.append(background_left_ampa)
self.connections.append(background_right_ampa)
if self.visual_cortex_input is not None:
# Task input -> E population connections
vc_left_lip_ampa = init_connection(self.visual_cortex_input[0],
self.left_lip.e_contra_vis,
'g_ampa_x',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_v_ec_vis,
delay=270 * ms)
vc_right_lip_ampa = init_connection(self.visual_cortex_input[1],
self.right_lip.e_contra_vis,
'g_ampa_x',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_v_ec_vis,
delay=270 * ms)
self.connections.append(vc_left_lip_ampa)
self.connections.append(vc_right_lip_ampa)
if self.go_input is not None:
go_left_lip_i_ampa = init_connection(self.go_input,
self.left_lip.i_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_i,
delay=5 * ms)
go_right_lip_i_ampa = init_connection(self.go_input,
self.right_lip.i_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_i,
delay=5 * ms)
go_left_lip_e_ampa = init_connection(self.go_input,
self.left_lip.e_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_e,
delay=5 * ms)
go_right_lip_e_ampa = init_connection(self.go_input,
self.right_lip.e_group,
'g_ampa_g',
self.params.w_ampa_min,
self.params.w_ampa_max,
self.params.p_g_e,
delay=5 * ms)
self.connections.append(go_left_lip_i_ampa)
self.connections.append(go_right_lip_i_ampa)
self.connections.append(go_left_lip_e_ampa)
self.connections.append(go_right_lip_e_ampa)
def __init__(self, lip_size, params, background_inputs=None, visual_cortex_input=None,
go_input=None):
self.lip_size=lip_size
self.N=2*self.lip_size
self.params=params
self.background_inputs=background_inputs
self.visual_cortex_input=visual_cortex_input
self.go_input=go_input
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
# AMPA conductance - recurrent input current
eqs += exp_synapse('g_ampa_r', params.tau_ampa, siemens)
eqs += Current('I_ampa_r=g_ampa_r*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse('g_ampa_b', params.tau_ampa, siemens)
eqs += Current('I_ampa_b=g_ampa_b*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse('g_ampa_x', params.tau_ampa, siemens)
eqs += Current('I_ampa_x=g_ampa_x*(E-vm): amp', E=params.E_ampa)
# AMPA conductance - go input current
eqs += exp_synapse('g_ampa_g', params.tau_ampa, siemens)
eqs += Current('I_ampa_g=g_ampa_g*(E-vm): amp', E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse('g_nmda', params.tau1_nmda, params.tau2_nmda, siemens)
eqs += Equations('g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ', Mg=params.Mg)
eqs += Current('I_nmda=g_V*g_nmda*(E-vm): amp', E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse('g_gaba_a', params.tau_gaba_a, siemens)
eqs += Current('I_gaba_a=g_gaba_a*(E-vm): amp', E=params.E_gaba_a)
# GABA-B conductance
eqs += biexp_synapse('g_gaba_b', params.tau1_gaba_b, params.tau2_gaba_b, siemens)
eqs += Current('I_gaba_b=g_gaba_b*(E-vm): amp', E=params.E_gaba_b)
# Total synaptic conductance
eqs += Equations('g_syn=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda+g_gaba_a+g_gaba_b : siemens')
eqs += Equations('g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_g+g_ampa_b+g_V*g_nmda : siemens')
# Total synaptic current
eqs += Equations('I_abs=abs(I_ampa_r)+abs(I_ampa_b)+abs(I_ampa_x)+abs(I_ampa_g)+abs(I_nmda)+abs(I_gaba_a) : amp')
NeuronGroup.__init__(self, self.N, model=eqs, threshold=-20*mV, reset=params.EL, compile=True)
self.init_subpopulations()
self.connections=[]
self.init_connectivity()
if self.background_inputs is not None:
# Background -> E+I population connections
background_left_ampa=init_connection(self.background_inputs[0], self.left_lip.neuron_group, 'g_ampa_b',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_b_e, delay=5*ms)
background_right_ampa=init_connection(self.background_inputs[1], self.right_lip.neuron_group, 'g_ampa_b',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_b_e, delay=5*ms)
self.connections.append(background_left_ampa)
self.connections.append(background_right_ampa)
if self.visual_cortex_input is not None:
# Task input -> E population connections
vc_left_lip_ampa=init_connection(self.visual_cortex_input[0], self.left_lip.e_contra_vis, 'g_ampa_x',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_v_ec_vis, delay=270*ms)
vc_right_lip_ampa=init_connection(self.visual_cortex_input[1], self.right_lip.e_contra_vis, 'g_ampa_x',
self.params.w_ampa_min, self.params.w_ampa_max, self.params.p_v_ec_vis, delay=270*ms)
self.connections.append(vc_left_lip_ampa)
self.connections.append(vc_right_lip_ampa)
if self.go_input is not None:
go_left_lip_i_ampa=init_connection(self.go_input, self.left_lip.i_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_i, delay=5*ms)
go_right_lip_i_ampa=init_connection(self.go_input, self.right_lip.i_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_i, delay=5*ms)
go_left_lip_e_ampa=init_connection(self.go_input, self.left_lip.e_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_e, delay=5*ms)
go_right_lip_e_ampa=init_connection(self.go_input, self.right_lip.e_group, 'g_ampa_g', self.params.w_ampa_min,
self.params.w_ampa_max, self.params.p_g_e, delay=5*ms)
self.connections.append(go_left_lip_i_ampa)
self.connections.append(go_right_lip_i_ampa)
self.connections.append(go_left_lip_e_ampa)
self.connections.append(go_right_lip_e_ampa)
def __init__(
self,
params=default_params,
pyr_params=pyr_params(),
inh_params=inh_params(),
plasticity_params=plasticity_params(),
background_input=None,
task_inputs=None,
clock=defaultclock,
):
self.params = params
self.pyr_params = pyr_params
self.inh_params = inh_params
self.plasticity_params = plasticity_params
self.background_input = background_input
self.task_inputs = task_inputs
## Set up equations
# Exponential integrate-and-fire neuron
eqs = exp_IF(params.C, params.gL, params.EL, params.VT, params.DeltaT)
eqs += Equations("g_muscimol : nS")
# AMPA conductance - recurrent input current
eqs += exp_synapse("g_ampa_r", params.tau_ampa, siemens)
eqs += Current("I_ampa_r=g_ampa_r*(E-vm): amp", E=params.E_ampa)
# AMPA conductance - background input current
eqs += exp_synapse("g_ampa_b", params.tau_ampa, siemens)
eqs += Current("I_ampa_b=g_ampa_b*(E-vm): amp", E=params.E_ampa)
# AMPA conductance - task input current
eqs += exp_synapse("g_ampa_x", params.tau_ampa, siemens)
eqs += Current("I_ampa_x=g_ampa_x*(E-vm): amp", E=params.E_ampa)
# Voltage-dependent NMDA conductance
eqs += biexp_synapse("g_nmda", params.tau1_nmda, params.tau2_nmda, siemens)
eqs += Equations("g_V = 1/(1+(Mg/3.57)*exp(-0.062 *vm/mV)) : 1 ", Mg=params.Mg)
eqs += Current("I_nmda=g_V*g_nmda*(E-vm): amp", E=params.E_nmda)
# GABA-A conductance
eqs += exp_synapse("g_gaba_a", params.tau_gaba_a, siemens)
eqs += Current("I_gaba_a=(g_gaba_a+g_muscimol)*(E-vm): amp", E=params.E_gaba_a)
eqs += InjectedCurrent("I_dcs: amp")
# Total synaptic conductance
eqs += Equations("g_syn=g_ampa_r+g_ampa_x+g_ampa_b+g_V*g_nmda+g_gaba_a : siemens")
eqs += Equations("g_syn_exc=g_ampa_r+g_ampa_x+g_ampa_b+g_V*g_nmda : siemens")
# Total synaptic current
eqs += Equations(
"I_abs=(I_ampa_r**2)**.5+(I_ampa_b**2)**.5+(I_ampa_x**2)**.5+(I_nmda**2)**.5+(I_gaba_a**2)**.5 : amp"
)
NeuronGroup.__init__(
self,
params.network_group_size,
model=eqs,
threshold=-20 * mV,
refractory=1 * ms,
reset=params.Vr,
compile=True,
freeze=True,
clock=clock,
)
self.init_subpopulations()
self.init_connectivity(clock)