Motion-Clouds | Laurent Perrinet

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

Tue, 01 Nov 2016 00:00:00 +0000

Sparse coding of images in the retina follows regular statistics at the global, not the local scale

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
# PARAMETERS
seed = 2042
np.random.seed(seed=seed)
N_sparse = 5
sparse_base = 2.e5
sparseness = np.logspace(-1, 0, N_sparse, base=sparse_base, endpoint=True)
print(sparseness)
# TEXTON
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)
 axs[i_ax].set_xticks([])
 axs[i_ax].set_yticks([])
plt.tight_layout()
fig.subplots_adjust(hspace = .0, wspace = .0, left=0.0, bottom=0., right=1., top=1.)

Spatiotemporal tuning of retinal ganglion cells dependent on the context of signal presentation

Thu, 01 Jan 2015 00:00:00 +0000

Follow-up paper
Cesar U Ravello, Laurent U Perrinet, Maria-José Escobar, Adrián G Palacios (2019). Speed-Selectivity in Retinal Ganglion Cells is Sharpened by Broad Spatial Frequency, Naturalistic Stimuli. Scientific Reports.
Cite DOI Press URL HAL

Motion Clouds: Model-based stimulus synthesis of natural-like random textures for the study of motion perception

Wed, 14 Mar 2012 00:00:00 +0000

MotionClouds are random dynamic stimuli optimized to study motion perception.

Web-site
Source code using Python.
37 citations on Google Scholar (last updated 22/10/2021)
Supplementary information
Follow-up paper
Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2018). Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures. Neural Computation.
PDF Cite DOI URL arXiv

Jonathan Vacher, Andrew Isaac Meso, Laurent U Perrinet, Gabriel Peyré (2015). Biologically Inspired Dynamic Textures for Probing Motion Perception. Advances in Neural Information Processing Systems.
Cite Pdf PDF arXiv
This library was notably used in the following paper
Claudio Simoncini, Laurent U Perrinet, Anna Montagnini, Pascal Mamassian, Guillaume S Masson (2012). More is not always better: dissociation between perception and action explained by adaptive gain control. Nature Neuroscience.
PDF Cite DOI URL

Figure 4 Broadband vs. narrowband stimuli. From A through B to C, the frequency bandwidth Bf increases, while all other parameters (such as f0) are kept constant. The MC with the broadest bandwidth is thought to best represent natural stimuli, since, as those, it contains many frequency components. A: Bf = 0:05 (Supplemental Movie S4). B: Bf = 0:15 (Supplemental Movie S5). C: Bf = 0:4 (Supplemental Movie S6).