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