net = nef.Network('Test', fixed_seed=1)
input = net.make_input('input', [0])
A = net.make('A',
neurons=100,
dimensions=1,
max_rate=(100, 150),
radius=1,
intercept=(-.9, .9))
B = net.make('B',
neurons=200,
dimensions=1,
max_rate=(100, 150),
radius=1,
intercept=(-.9, .9))
C = net.make('C',
neurons=100,
dimensions=1,
max_rate=(100, 150),
radius=1,
intercept=(-.9, .9))
net.connect(input, A)
net.connect(A, B)
net.connect(B, C)
s = spinn.SpiNN(net.network)
s.print_info()
s.write_to_file('nengo_spinnaker_interface/nengo_values.py')
"""
import nef
# importing the Nengo/SpiNNakerinterface module
import nengo_spinnaker_interface.spinn as spinn
net=nef.Network('SpiNN Integrator',quick=False)
input=net.make_input('input',[0])
tau_syn = .2
n = 100
A=net.make('A', neurons=n, dimensions=1, max_rate=(120,150), intercept=(-.90,.90))
B=net.make('B', neurons=n*2, dimensions=1, max_rate=(120,150), intercept=(-.90,.90))
C=net.make('C', neurons=n, dimensions=1, max_rate=(120,150), intercept=(-.90,.90))
net.connect(input,A,pstc=.001)
net.connect(A,B,pstc=tau_syn, weight=tau_syn)
net.connect(B,B,pstc=tau_syn)
net.connect(B,C)
s=spinn.SpiNN(net.network) # instantiates a SpiNNaker/NEF network
s.print_info() # prints informations about the nodes
s.write_to_file('nengo_spinnaker_interface/nengo_values.py') # dumps network values into a python file