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,
     ])
Exemple #2
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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()

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,
Exemple #3
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different connection schemes
'''
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)