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=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=1000, nonlin_order=3): # Create Gabor kernel rfsize = (25, 25) K_true = createRF(name='gabor', size=rfsize, threshold=0.2, dtype=np.float64, frequency=0.5, sigma=2*[0.35]) # GWN Stimulus) X = createGratings(size=rfsize, N=N, center=True, whiten=True) # Poisson spike generation z = np.dot(X, K_true.ravel()) z[z < 0] = 0 z = z ** nonlin_order z /= z.max() Y = (z > np.random.rand(N)).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=1000, nonlin_order=3): # Create Gabor kernel rfsize = (25, 25) K_true = createRF(name='gabor', size=rfsize, threshold=0.2, dtype=np.float64, frequency=0.5, sigma=2 * [0.35]) # GWN Stimulus) X = createGratings(size=rfsize, N=N, center=True, whiten=True) # Poisson spike generation z = np.dot(X, K_true.ravel()) z[z < 0] = 0 z = z**nonlin_order z /= z.max() Y = (z > np.random.rand(N)).astype(np.float64) print "%d spikes (p(spike) = %0.3f)" % (np.sum(Y > 0), np.mean(Y > 0)) return X, Y, K_true