def initialize_network(self, params):
allBut1 = schemes.get("allBut1")
all2all = schemes.get("all2all")
Input = population.Image_Input(28, 28)
c_params = {
"eta": params.get("eta", 0.0005),
"mu": params.get("mu", 0.05),
}
L1 = population.Population(num_neurons=params.get("num_neurons", 16),
v_init=params.get("v_init", -65),
v_decay=params.get("v_decay", .99),
v_reset=params.get("v_reset", -65),
min_volt=params.get("v_rest", -65),
t_init=params.get("min_volt", -50),
min_thresh=params.get("min_thresh", -52),
t_bias=params.get("t_bias", 0.25),
t_decay=params.get("t_decay", .9999999),
trace_decay=params.get("trace_decay", 0.95),
refrac=params.get("refrac", 5),
one_spike=params.get("one_spike", True))
inh = params.get("inh", -120)
C1 = Connection(Input,
L1,
0.3 * all2all(Input.num_neurons, L1.num_neurons),
params,
rule="PreAndPost",
wmin=0,
wmax=1)
C2 = Connection(L1,
L1,
allBut1(L1.num_neurons) * inh,
params,
rule="static",
wmin=inh,
wmax=0)
self.network = Network([
Input,
L1,
], [
C1,
C2,
])
(x_train, y_train), (x_test, y_test) = mnist.load_data()
for x in x_train:
x = x.astype(float) * 2
'''
Initialize populations
'''
Input = population.Image_Input()
L1 = population.Population(num_neurons=100,
v_init=-65,
v_decay=.99,
v_reset=-60,
min_volt=-65,
t_init=-52,
min_thresh=-52,
t_bias=0.05,
t_decay=.9999999,
refrac=5,
trace_decay=.95,
one_spike=True)
L2 = population.Population(
num_neurons=100,
v_init=-65,
v_decay=.9,
v_reset=-45,
min_volt=-60,
t_init=-40,
adapt_thresh=False,
refrac=2,
trace_decay=.95,
all2all = schemes.get("all2all")
one2one = schemes.get("one2one")
local = schemes.get("local")
(x_train, y_train), (x_test, y_test) = mnist.load_data()
'''
Initialize populations
'''
Input = population.Image_Input()
L1 = population.Population(num_neurons=150,
v_init=-65,
v_decay=.99,
v_reset=-60,
min_volt=-65,
t_init=-52,
min_thresh=-52,
t_bias=0.25,
t_decay=.9999999,
refrac=5,
trace_decay=.95,
one_spike=True)
'''
Initialize connections
'''
inh = -120
C1 = Connection(Input,
L1,
0.3 * all2all(Input.num_neurons, L1.num_neurons),
params,
rule="PreAndPost",
wmin=0,
'''
rand = schemes.get("random")
allBut1 = schemes.get("allBut1")
grid = schemes.get("grid")
all2all = schemes.get("all2all")
one2one = schemes.get("one2one")
local = schemes.get("local")
(x_train, y_train), (x_test, y_test) = mnist.load_data()
'''
Initialize default populations
'''
Input = population.Image_Input(28,28)
L1 = population.Population(num_neurons = 100)
L2 = population.Population(num_neurons = 100)
'''
Initialize connections
'''
inh = -120
C1 = Connection(Input,
L1,
0.3 * all2all(Input.num_neurons, L1.num_neurons),
params,
rule = "PreAndPost",
wmin = 0,
wmax = 1)
C2 = Connection(L1,