examples/example_24_large_scale_brian2_simulation/clusternet.py
def add_parameters(traj):
        """Adds all neuron group parameters to `traj`."""
        assert(isinstance(traj,Trajectory))

        scale = traj.simulation.scale


        traj.v_standard_parameter = Brian2Parameter

        model_eqs = '''dV/dt= 1.0/tau_POST * (mu - V) + I_syn : 1
                       mu : 1
                       I_syn =  - I_syn_i + I_syn_e : Hz
                    '''

        conn_eqs = '''I_syn_PRE = x_PRE/(tau2_PRE-tau1_PRE) : Hz
                      dx_PRE/dt = -(normalization_PRE*y_PRE+x_PRE)*invtau1_PRE : 1
                      dy_PRE/dt = -y_PRE*invtau2_PRE : 1
                   '''

        traj.f_add_parameter('model.eqs', model_eqs,
                           comment='The differential equation for the neuron model')

        traj.f_add_parameter('model.synaptic.eqs', conn_eqs,
                           comment='The differential equation for the synapses. '
                                   'PRE will be replaced by `i` or `e` depending '
                                   'on the source population')

        traj.f_add_parameter('model.synaptic.tau1', 1*ms, comment = 'The decay time')
        traj.f_add_parameter('model.synaptic.tau2_e', 3*ms, comment = 'The rise time, excitatory')
        traj.f_add_parameter('model.synaptic.tau2_i', 2*ms, comment = 'The rise time, inhibitory')

        traj.f_add_parameter('model.V_th', 'V >= 1.0', comment = "Threshold value")
        traj.f_add_parameter('model.reset_func', 'V=0.0',
                             comment = "String representation of reset function")
        traj.f_add_parameter('model.refractory', 5*ms, comment = "Absolute refractory period")

        traj.f_add_parameter('model.N_e', int(2000*scale), comment = "Amount of excitatory neurons")
        traj.f_add_parameter('model.N_i', int(500*scale), comment = "Amount of inhibitory neurons")

        traj.f_add_parameter('model.tau_e', 15*ms, comment = "Membrane time constant, excitatory")
        traj.f_add_parameter('model.tau_i', 10*ms, comment = "Membrane time constant, inhibitory")

        traj.f_add_parameter('model.mu_e_min', 1.1, comment = "Lower bound for bias, excitatory")
        traj.f_add_parameter('model.mu_e_max', 1.2, comment = "Upper bound for bias, excitatory")

        traj.f_add_parameter('model.mu_i_min', 1.0, comment = "Lower bound for bias, inhibitory")
        traj.f_add_parameter('model.mu_i_max', 1.05, comment = "Upper bound for bias, inhibitory")
def add_parameters(traj):
        """Adds all neuron group parameters to `traj`."""
        assert(isinstance(traj,Trajectory))

        scale = traj.simulation.scale


        traj.v_standard_parameter = Brian2Parameter

        model_eqs = '''dV/dt= 1.0/tau_POST * (mu - V) + I_syn : 1
                       mu : 1
                       I_syn =  - I_syn_i + I_syn_e : Hz
                    '''

        conn_eqs = '''I_syn_PRE = x_PRE/(tau2_PRE-tau1_PRE) : Hz
                      dx_PRE/dt = -(normalization_PRE*y_PRE+x_PRE)*invtau1_PRE : 1
                      dy_PRE/dt = -y_PRE*invtau2_PRE : 1
                   '''

        traj.f_add_parameter('model.eqs', model_eqs,
                           comment='The differential equation for the neuron model')

        traj.f_add_parameter('model.synaptic.eqs', conn_eqs,
                           comment='The differential equation for the synapses. '
                                   'PRE will be replaced by `i` or `e` depending '
                                   'on the source population')

        traj.f_add_parameter('model.synaptic.tau1', 1*ms, comment = 'The decay time')
        traj.f_add_parameter('model.synaptic.tau2_e', 3*ms, comment = 'The rise time, excitatory')
        traj.f_add_parameter('model.synaptic.tau2_i', 2*ms, comment = 'The rise time, inhibitory')

        traj.f_add_parameter('model.V_th', 'V >= 1.0', comment = "Threshold value")
        traj.f_add_parameter('model.reset_func', 'V=0.0',
                             comment = "String representation of reset function")
        traj.f_add_parameter('model.refractory', 5*ms, comment = "Absolute refractory period")

        traj.f_add_parameter('model.N_e', int(2000*scale), comment = "Amount of excitatory neurons")
        traj.f_add_parameter('model.N_i', int(500*scale), comment = "Amount of inhibitory neurons")

        traj.f_add_parameter('model.tau_e', 15*ms, comment = "Membrane time constant, excitatory")
        traj.f_add_parameter('model.tau_i', 10*ms, comment = "Membrane time constant, inhibitory")

        traj.f_add_parameter('model.mu_e_min', 1.1, comment = "Lower bound for bias, excitatory")
        traj.f_add_parameter('model.mu_e_max', 1.2, comment = "Upper bound for bias, excitatory")

        traj.f_add_parameter('model.mu_i_min', 1.0, comment = "Lower bound for bias, inhibitory")
        traj.f_add_parameter('model.mu_i_max', 1.05, comment = "Upper bound for bias, inhibitory")
def add_parameters ( traj ) : assert ( isinstance ( traj , Trajectory ) ) scale = traj . simulation . scale traj . v_standard_parameter = Brian2Parameter model_eqs = '''dV/dt= 1.0/tau_POST * (mu - V) + I_syn : 1
                       mu : 1
                       I_syn =  - I_syn_i + I_syn_e : Hz
                    ''' conn_eqs = '''I_syn_PRE = x_PRE/(tau2_PRE-tau1_PRE) : Hz
                      dx_PRE/dt = -(normalization_PRE*y_PRE+x_PRE)*invtau1_PRE : 1
                      dy_PRE/dt = -y_PRE*invtau2_PRE : 1
                   ''' traj . f_add_parameter ( 'model.eqs' , model_eqs , comment = 'The differential equation for the neuron model' ) traj . f_add_parameter ( 'model.synaptic.eqs' , conn_eqs , comment = 'The differential equation for the synapses. ' 'PRE will be replaced by `i` or `e` depending ' 'on the source population' ) traj . f_add_parameter ( 'model.synaptic.tau1' , 1 * ms , comment = 'The decay time' ) traj . f_add_parameter ( 'model.synaptic.tau2_e' , 3 * ms , comment = 'The rise time, excitatory' ) traj . f_add_parameter ( 'model.synaptic.tau2_i' , 2 * ms , comment = 'The rise time, inhibitory' ) traj . f_add_parameter ( 'model.V_th' , 'V >= 1.0' , comment = "Threshold value" ) traj . f_add_parameter ( 'model.reset_func' , 'V=0.0' , comment = "String representation of reset function" ) traj . f_add_parameter ( 'model.refractory' , 5 * ms , comment = "Absolute refractory period" ) traj . f_add_parameter ( 'model.N_e' , int ( 2000 * scale ) , comment = "Amount of excitatory neurons" ) traj . f_add_parameter ( 'model.N_i' , int ( 500 * scale ) , comment = "Amount of inhibitory neurons" ) traj . f_add_parameter ( 'model.tau_e' , 15 * ms , comment = "Membrane time constant, excitatory" ) traj . f_add_parameter ( 'model.tau_i' , 10 * ms , comment = "Membrane time constant, inhibitory" ) traj . f_add_parameter ( 'model.mu_e_min' , 1.1 , comment = "Lower bound for bias, excitatory" ) traj . f_add_parameter ( 'model.mu_e_max' , 1.2 , comment = "Upper bound for bias, excitatory" ) traj . f_add_parameter ( 'model.mu_i_min' , 1.0 , comment = "Lower bound for bias, inhibitory" ) traj . f_add_parameter ( 'model.mu_i_max' , 1.05 , comment = "Upper bound for bias, inhibitory" )
Adds all neuron group parameters to `traj`.
Adds all neuron group parameters to traj .