def main():
duration = 1000.0
N = NeuronGroup(10)
rate = 500.0 # Hz
dt = 1.0 / 1000.0 # 1 ms
rate_ms = rate * dt
for now in np.arange(0.0, duration, 1.0):
input_rate = (np.random.rand(N.size) < rate_ms).astype(floatX) * 125.0
N.tick(now, input_rate)
print(now, N.rate.get_value().mean())
def main():
duration = 1000.0
N = NeuronGroup(10)
rate = 500.0 # Hz
dt = 1.0 / 1000.0 # 1 ms
rate_ms = rate * dt
for now in np.arange(0.0, duration, 1.0):
input_rate = (np.random.rand(N.size) < rate_ms).astype(floatX) * 125.0
N.tick(now, input_rate)
print(now, N.rate.get_value().mean())
def main():
duration = 1000.0
N = NeuronGroup(1000)
S = SynapseGroup(N, N)
S.weight.set_value(np.random.rand(N.size, N.size).astype(floatX) * 20.0 - 10.0)
# TODO: scan + batch 20ms intervals onto the GPU
# http://www.deeplearning.net/tutorial/logreg.html#defining-a-loss-function
start = time.clock()
for now in np.arange(0.0, duration, 1.0):
N.tick(now, np.random.rand(N.size).astype(floatX) * 5.0)
S.tick(now)
weights = S.weight.get_value()
end = time.clock()
print ("t", now, "real", end - start, "rate", np.count_nonzero(N.spikes))
def main():
duration = 3.14 * 1000.0
N = NeuronGroup(100)
dt = 1.0 / 1000.0 # 1 ms
noise_rate_ms = 3.0 * dt
encoding = COMEstimator(N.size, 3.0, 1.0)
for now in np.arange(0.0, duration, 1.0):
input_value = math.sin(now / 1000.0)
input_norm = normalize(input_value, -1.0, 1.0)
input_rate = (np.random.rand(N.size) < noise_rate_ms).astype(floatX) * 125.0
encoding.encode(input_rate, input_norm)
N.tick(now, input_rate)
output_norm = encoding.decode(N.rate.get_value())
output_value = denormalize(output_norm, -1.0, 1.0)
err = abs(input_norm - output_norm)
print (now, err, input_value, output_value)
def main():
duration = 3.14 * 1000.0
N = NeuronGroup(100)
dt = 1.0 / 1000.0 # 1 ms
noise_rate_ms = 3.0 * dt
encoding = COMEstimator(N.size, 3.0, 1.0)
for now in np.arange(0.0, duration, 1.0):
input_value = math.sin(now / 1000.0)
input_norm = normalize(input_value, -1.0, 1.0)
input_rate = (np.random.rand(N.size) <
noise_rate_ms).astype(floatX) * 125.0
encoding.encode(input_rate, input_norm)
N.tick(now, input_rate)
output_norm = encoding.decode(N.rate.get_value())
output_value = denormalize(output_norm, -1.0, 1.0)
err = abs(input_norm - output_norm)
print(now, err, input_value, output_value)
def main():
training_duration = 3.14 * 1000.0 * 3
testing_duration = 3.14 * 1000.0 * 1
N1 = NeuronGroup(100)
N2 = NeuronGroup(50)
S1 = SynapseGroup(N1, N2)
S2 = SynapseGroup(N1, N2)
weights = np.random.rand(N1.size, N2.size).astype(floatX)
weights = denormalize(weights, -4.0, 0.0)
S1.weight.set_value(weights)
weights = np.random.rand(N1.size, N2.size).astype(floatX)
weights = denormalize(weights, 0.0, 4.0)
S2.weight.set_value(weights)
dt = 1.0 / 1000.0 # 1 ms
noise_rate_ms = 5.0 * dt
encoding = COMEstimator(N1.size, 3.0, 1.0)
decoding = COMEstimator(N2.size, 3.0, 1.0)
# training
for now in np.arange(0.0, training_duration, 1.0):
input_value = math.sin(now / 1000.0)
input_norm = normalize(input_value, -1.0, 1.0)
output_value = -input_value
output_norm = normalize(output_value, -1.0, 1.0)
input_rate = (np.random.rand(N1.size) < noise_rate_ms).astype(floatX) * 125.0
output_rate = (np.random.rand(N2.size) < noise_rate_ms).astype(floatX) * 125.0
encoding.encode(input_rate, input_norm)
decoding.encode(output_rate, output_norm)
N1.tick(now, input_rate)
N2.tick(now, output_rate)
S1.tick(now, True, False)
S2.tick(now, True, False)
output_norm_actual = decoding.decode(N2.rate.get_value())
err = abs(output_norm - output_norm_actual)
print ("training", now / training_duration, err)
# testing
mse = 0.0
for now in np.arange(0.0, testing_duration, 1.0):
input_value = math.sin(now / 1000.0)
input_norm = normalize(input_value, -1.0, 1.0)
input_rate = np.zeros(N1.size).astype(floatX)
output_rate = np.zeros(N2.size).astype(floatX)
encoding.encode(input_rate, input_norm)
N1.tick(now, input_rate)
N2.tick(now, output_rate)
S1.tick(now, False, True)
S2.tick(now, False, True)
output_value = -input_value
output_norm = normalize(output_value, -1.0, 1.0)
output_norm_actual = decoding.decode(N2.rate.get_value())
output_value_actual = denormalize(output_norm_actual, -1.0, 1.0)
err = abs(output_norm - output_norm_actual)
mse += math.pow(err, 2.0)
print ("testing", now / testing_duration, err)
mse /= testing_duration
print ("mse", mse)
def main():
training_duration = 3.14 * 1000.0 * 3
testing_duration = 3.14 * 1000.0 * 1
N1 = NeuronGroup(100)
N2 = NeuronGroup(50)
S1 = SynapseGroup(N1, N2)
S2 = SynapseGroup(N1, N2)
weights = np.random.rand(N1.size, N2.size).astype(floatX)
weights = denormalize(weights, -4.0, 0.0)
S1.weight.set_value(weights)
weights = np.random.rand(N1.size, N2.size).astype(floatX)
weights = denormalize(weights, 0.0, 4.0)
S2.weight.set_value(weights)
dt = 1.0 / 1000.0 # 1 ms
noise_rate_ms = 5.0 * dt
encoding = COMEstimator(N1.size, 3.0, 1.0)
decoding = COMEstimator(N2.size, 3.0, 1.0)
# training
for now in np.arange(0.0, training_duration, 1.0):
input_value = math.sin(now / 1000.0)
input_norm = normalize(input_value, -1.0, 1.0)
output_value = input_value
output_norm = normalize(output_value, -1.0, 1.0)
input_rate = (np.random.rand(N1.size) <
noise_rate_ms).astype(floatX) * 125.0
output_rate = (np.random.rand(N2.size) <
noise_rate_ms).astype(floatX) * 125.0
encoding.encode(input_rate, input_norm)
decoding.encode(output_rate, output_norm)
N1.tick(now, input_rate)
N2.tick(now, output_rate)
S1.tick(now, True, False)
S2.tick(now, True, False)
output_norm = decoding.decode(N2.rate.get_value())
err = abs(input_norm - output_norm)
print("training", now / training_duration, err)
# testing
mse = 0.0
for now in np.arange(0.0, testing_duration, 1.0):
input_value = math.sin(now / 1000.0)
input_norm = normalize(input_value, -1.0, 1.0)
input_rate = np.zeros(N1.size).astype(floatX)
output_rate = np.zeros(N2.size).astype(floatX)
encoding.encode(input_rate, input_norm)
N1.tick(now, input_rate)
N2.tick(now, output_rate)
S1.tick(now, False, True)
S2.tick(now, False, True)
output_norm = decoding.decode(N2.rate.get_value())
output_value = denormalize(output_norm, -1.0, 1.0)
err = abs(input_norm - output_norm)
mse += math.pow(err, 2.0)
print("testing", now / testing_duration, err)
mse /= testing_duration
print("mse", mse)
