c_cp_45.plot(colorLimits=[0, 150]) pylab.show() # ! Note that the NMDA projection virtually vanishes for V_m=-75mV, but is very # ! strong for V_m=-45mV. GABA_A and GABA_B projections are also stronger, # ! while AMPA is weaker for V_m=-45mV. # ! Non-Dale network model # ! ====================== # ! By default, ConnPlotter assumes that networks follow Dale's law, i.e., # ! either make excitatory or inhibitory connections. If this assumption # ! is violated, we need to inform ConnPlotter how synapse types are grouped. # ! We look at a simple example here. # ! Load model nd_layer, nd_conn, nd_model = ex.non_dale() # ! We specify the synapse configuration using the synTypes argument: # ! # ! - synTypes is a tuple. # ! - Each element in the tuple represents a group of synapse models # ! - Any sender can make connections with synapses from **one group only**. # ! - Each synapse model is specified by a ``SynType``. # ! - The SynType constructor takes three arguments: # ! # ! * The synapse model name # ! * The weight to apply then aggregating across synapse models # ! * The color to use for the synapse type # ! # ! - Synapse names must be unique, and must form a superset of all synapse # ! models in the network.
c_cp_45.plot(colorLimits=[0, 150]) plt.show() # ! Note that the NMDA projection virtually vanishes for V_m=-75mV, but is very # ! strong for V_m=-45mV. GABA_A and GABA_B projections are also stronger, # ! while AMPA is weaker for V_m=-45mV. # ! Non-Dale network model # ! ====================== # ! By default, ConnPlotter assumes that networks follow Dale's law, i.e., # ! either make excitatory or inhibitory connections. If this assumption # ! is violated, we need to inform ConnPlotter how synapse types are grouped. # ! We look at a simple example here. # ! Load model nd_layer, nd_conn, nd_model = ex.non_dale() # ! We specify the synapse configuration using the synTypes argument: # ! # ! - synTypes is a tuple. # ! - Each element in the tuple represents a group of synapse models # ! - Any sender can make connections with synapses from **one group only**. # ! - Each synapse model is specified by a ``SynType``. # ! - The SynType constructor takes three arguments: # ! # ! * The synapse model name # ! * The weight to apply then aggregating across synapse models # ! * The color to use for the synapse type # ! # ! - Synapse names must be unique, and must form a superset of all synapse # ! models in the network.