Differential response of the retinal neural code with respect to the sparseness of natural images

See supplementray code.

How does the retina respond to stimuli with different sparseness?

This stimulus is generated simply using the Motion Clouds library by defining a sparse draw of events:

import numpy as np
import MotionClouds as mc
import matplotlib.pyplot as plt
seed = 2042
N_sparse = 5
sparse_base = 2.e5
sparseness =  np.logspace(-1, 0, N_sparse, base=sparse_base, endpoint=True)
N_X, N_Y, N_frame = 256, 256, 1
fx, fy, ft = mc.get_grids(N_X, N_Y, 1)
mc_i = mc.envelope_gabor(fx, fy, ft, sf_0=0.05, B_sf=0.025, B_theta=np.inf)
values = np.random.randn(N_X, N_Y, N_frame)
chance = np.argsort(-np.abs(values.ravel()))
chance = np.array(chance, dtype=np.float)
chance /= chance.max()
chance = chance.reshape((N_X, N_Y, N_frame))
fig, axs = plt.subplots(1, N_sparse, figsize=(fig_width, fig_width/N_sparse))
for i_ax, l0_norm in enumerate(sparseness):
    threshold = 1 - l0_norm
    mask = np.zeros_like(chance)
    mask[chance > threshold] = 1.
    im = 2*mc.rectif(mc.random_cloud(mc_i, events=mask*values))-1
    axs[i_ax].imshow(im[:, :, 0], vmin=-1, vmax=1, cmap=plt.gray())
    #axs[i_ax].text(9, 80, r'$n=%.0f\%%$' % (noise*100), color='white', fontsize=10)
    axs[i_ax].text(4, 40, r'$\epsilon=%.0e$' % l0_norm, color='white', fontsize=8)
fig.subplots_adjust(hspace = .0, wspace = .0, left=0.0, bottom=0., right=1., top=1.)
Laurent U Perrinet
Laurent U Perrinet
Researcher in Computational Neuroscience

My research interests include Machine Learning and computational neuroscience applied to Vision.