'EINs': eins,
'IINs': iins
},
edges=[('PCs/Op_exc_syn/r', 'EINs/Op_exc_syn/r_exc', None, {
'weight': 13.5
}),
('EINs/Op_exc_syn/r', 'PCs/Op_exc_syn/r_exc', None, {
'weight': 0.8 * 13.5
}),
('PCs/Op_exc_syn/r', 'IINs/Op_inh_syn/r_exc', None, {
'weight': 0.25 * 13.5
}),
('IINs/Op_inh_syn/r', 'PCs/Op_exc_syn/r_inh', None, {
'weight': 1.75 * 13.5
})])
jrc_ir = jrc_template.apply()
dt = 1e-3 # integration step size in s
dts = 1e-2 # variable storage sub-sampling step size in s
T = 42.
jrc = ComputeGraph(jrc_ir,
vectorization="nodes",
dt=dt,
backend='numpy',
name='JRC',
build_in_place=False)
w_ein_pc = np.linspace(0.5, 2, 10) * 0.8 * 13.5
w_iin_pc = np.linspace(0.5, 2, 10) * 1.75 * 13.5
""" Edges are defined by a list with four entries (1/2/3/4):
1) The source variable (PC/PRO/m_out refers to variable m_out in operator PRO of node PC) /
2) The target variable /
3) An edge template with additional operators (null means no particular edge template is used)
4) A dictionary of variables and values that are specific to this edge"""
circuit = CircuitTemplate(
name="JRC", nodes={'IIN': iin, 'EIN': ein, 'PC': pc},
edges = [ \
["PC/PRO/m_out", "IIN/RPO_e/m_in", None, {'weight': 33.75}],
["PC/PRO/m_out", "EIN/RPO_e/m_in", None, {'weight': 135.}],
["EIN/PRO/m_out", "PC/RPO_e_pc/m_in", None, {'weight': 108.}],
["IIN/PRO/m_out", "PC/RPO_i/m_in", None, {'weight': 33.75}]],
path=None)
# Instantiate (apply) circuit
circuit = circuit.apply()
# # ---------------------------------------------------
# # Visualise the network
# # ---------------------------------------------------
# pos = nx.spring_layout(circuit.graph)
# nx.draw_shell(circuit.graph, with_labels=True, node_size=2000, arrowsize=30)
# plt.show()
# ---------------------------------------------------
# Run simulation
# ---------------------------------------------------
# Setup backend
T, dt = 10.0, 0.001
compute_graph = circuit.compile(vectorization="nodes", dt=dt, backend="numpy")
