Example #1
0
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)
Example #3
0
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()
Example #4
0
    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)
Example #5
0
    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)
Example #6
0
    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)
Example #7
0
    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)