def create_toy_data(N=1000, n_trials=1):
# Create Gabor kernel
rfsize = (15, 15)
K_true = createRF(name='gabor',
size=rfsize,
threshold=0.2,
dtype=np.float64,
frequency=0.5,
sigma=2 * [0.35])
K_true[K_true < 0] *= 1.25
# GWN Stimulus
X = np.random.randn(N, rfsize[0] * rfsize[1])
# Simulate response of LNP model
cell = SimpleCell(K_true,
threshold=1.5,
stddev=0.5,
n_trials=n_trials,
rectify=True)
Y = cell.simulate(X).astype(np.float64)
print "%d spikes (p(spike) = %0.3f)" % (np.sum(Y > 0), np.mean(Y > 0))
return X, Y, K_true
def create_toy_data(N=10, ndim=2):
k_true = np.zeros((ndim, 1))
k_true[::2] = -1
k_true[1::2] = 1
X = np.random.randn(N, ndim)
cell = SimpleCell(k_true,
threshold=.75,
stddev=0.1,
n_trials=1,
rectify=True,
dtype=np.float64,
seed=None)
y = cell.simulate(X)
return X, y, k_true
def create_toy_data(N=1000, n_trials=1):
# Create Gabor kernel
rfsize = (15, 15)
K_true = createRF(name='gabor', size=rfsize, threshold=0.2,
dtype=np.float64, frequency=0.5, sigma=2*[0.35])
K_true[K_true < 0] *= 1.25
# GWN Stimulus
X = np.random.randn(N, rfsize[0] * rfsize[1])
# Simulate response of LNP model
cell = SimpleCell(K_true, threshold=1.5, stddev=0.5, n_trials=n_trials,
rectify=True)
Y = cell.simulate(X).astype(np.float64)
print "%d spikes (p(spike) = %0.3f)" % (np.sum(Y > 0),
np.mean(Y > 0))
return X, Y, K_true
