# Mainen & Sejnowski, 1995

In this notebook, we will go from the learning numpy and matplotlib to our first neural simulator. As a case study, we will use the work from Mainen & Sejnowski (1995). The rest of this post is in french.

Le but ici de cette première tache est de créer un "raster plot" qui montre la reproducibilité d'un train de spike avec des répétitions du même stimulus. E, particulier, nous allons essayer de répliquer la figure 1 de Mainen & Sejnowski (1995). Travail fait en collaboration avec les étudiants du L3 Sciences et Humanités Vision Lumière Couleurs d'AMU (Benjamin Chassagne, Romane Renou, Hassina Bendrer, Giulia Danielou)

Ce notebook a été élaboré lors d'un TP dans le cadre de la licence Sciences & Humanités.

# Origins of the Von Mises distribution

The goal here is to check if the Von Mises distribution is the a priori choice to make when handling polar coordinates.

# Feature vs global motion

As we see a visual scene, there is contribution of the motion of each of the objects that constitute the visual scene into detecting its global motion. In particular, it is debatable to know which weight individual features, such as small objects in the foreground, have into this computation compared to a dense texture-like stimulus, as that of the background for instance.

Here, we design a a stimulus where we control independently these two aspects of motions to titrate their relative contribution to the detection of motion.

Can you spot the motion ? Is it more going to the upper left or to the upper right?

(For a more controlled test, imagine you fixate on the center of the movie.)

# Using regex to filter an ISO8601 date

It can be useful to find a pattern such an ISO8601-formatted date in a set of files. I discovered it is possible to do that in the atom editor:

# Embedding a trajectory in noise

MotionClouds may be considered as a control stimulus - it seems more interesting to consider more complex trajectories. Following a previous post, we design a trajectory embedded in noise.

Can you spot the motion ? from left to right or reversed ?

(For a more controlled test, imagine you fixate on the top left corner of each movie.)

(Upper row is coherent, lower row uncoherent / Left is -> Right column is <-)

TODO: use a biomimetic camouflage such as in this video.

# Rainbow effect

When I see a rainbow, I perceive the luminance inside the arc to be brighter than outside the arc. Is this effect percpetual (inside our head) or physical (inside each droplet in the sky). So, this is a simple notebook to show off how to synthesize the image of a rainbow on a realistic sky. TL;DR: there must be a physical reason for it.

Outline: The rainbow is a set of colors over a gradient of hues, masked for certain ones. The sky will be a gradient over blueish colors.

# Statistics of the natural input to a ring model

What does the input to a population of neurons in the primary visual cortex look like? In this post, we will try to have a feeling of the structure and statistics of the natural input to such a "ring" model.

This notebook explores this question using a retina-like temporal filtering and oriented Gabor-like filters. It produces this polar plot of the instantaneous energy in the different orientations for a natural movie :

One observes different striking features in the structure of this input to populations of V1 neurons:

• input is sparse: often, a few orientations dominate - the shape of these components (bandwidth) seem to be similar,
• there are many "switches": at some moments, the representations flips to another. This is due to cuts in the movie (changes from one scene to the other for instance). In a more realistic setting where we would add eye movements, these switches should also happen during saccades (but is there any knowledge of the occurence of the switch by the visual system?),
• between switches, there is some coherence in amplitude (a component will slowly change its energy) but also in time (a component is more likely to have a ghradually changing oriantation, for instance when the scene rotates).

This structure is specific to the structure of natural images and to the way they transform (translations, rotations, zooms due to the motion and deformation of visual objects). This is certainly incorporated as a "prior" information in the structure of the visual cortex. As to know how and where this is implemented is an open scientific question.

This is joint work with Hugo Ladret.

# extending datasets in pyTorch

PyTorch is a great library for machine learning. You can in a few lines of codes retrieve a dataset, define your model, add a cost function and then train your model. It's quite magic to copy and paste code from the internet and get the LeNet network working in a few seconds to achieve more than 98% accuracy.

However, it can be tedious sometimes to extend existing objects and here, I will manipulate some ways to define the right dataset for your application. In particular I will modify the call to a standard dataset (MNIST) to place the characters at random places in a large image.

# generating an unique seed for a given filename

When creating large simulations, you may sometimes create unique identifiers for each of it. This is useful to cache intermediate results for instance. This is the main function of hashes. We will here create a simple one-liner function to generate one.

# Testing more complex trajectories

MotionClouds may be considered as a control stimulus - it seems more interesting to consider more complex trajectories.

# predictive-coding of variable motion

In some recent modeling work:

Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):2726--50, 2012 https://laurentperrinet.github.io/publication/perrinet-12-pred

we study the role of transport in modifying our perception of motion. Here, we test what happens when we change the amount of noise in the stimulus.

In this script the predictive coding is done using the MotionParticles package and for a http://motionclouds.invibe.net/ within a disk aperture.

# accessing the data from a pupil recording

I am experimenting with the pupil eyetracker and could set it up (almost) smoothly on a macOS. There is an excellent documentation, and my first goal was to just record raw data and extract eye position.

In [1]:
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="https://laurentperrinet.github.io/sciblog/files/2017-12-13_pupil%20test_480.mp4" width=61.8%/></center>')

Out[1]:

This video shows the world view (cranio-centric, from a head-mounted camera fixed on the frame) with overlaid the position of the (right) eye while I am configuring a text box. You see the eye fixating on the screen then jumping somewhere else on the screen (saccades) or on the keyboard / hands. Note that the screen itself shows the world view, such that this generates an self-reccurrent pattern.

For this, I could use the capture script and I will demonstrate here how to extract the raw data in a few lines of python code.

# MEUL with a non-parametric homeostasis

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). Compared to other posts, such as this previous post, we improve the code to not depend on any parameter (namely the Cparameter of the rescaling function). For that, we will use a non-parametric approach based on the use of cumulative histograms.

This is joint work with Victor Boutin and Angelo Francisioni. See also the other posts on unsupervised learning.

# Designing a A0 poster using matplotlib

## Poster GDR Vision¶

This poster was presented in Lille at a vision workshop, check out https://laurentperrinet.github.io/publication/perrinet-17-gdr

Apart the content (which is in French) which recaps some previous work inbetween art and science, this post demonstrates how to generate a A0 poster programmatically. In particular, we will use matplotlib and some quickly forged functions to ease up the formatting.

# Improving calls to the LogGabor library

To code image as edges, for instance in the SparseEdges sparse coding scheme, we use a model of edges in images. A good model for these edges are bidimensional Log Gabor filter. This is implemented for instance in the LogGabor library. The library was designed to be precise, but not particularly for efficiency. In order to improve its speed, we demonstrate here the use of a cache to avoid redundant computations.

# The fastest 2D convolution in the world

Convolutions are essential components of any neural networks, image processing, computer vision ... but these are also a bottleneck in terms of computations... I will here benchmark different solutions using numpy, scipy or pytorch. This is work-in-progress, so that any suggestion is welcome, for instance on StackExchange or in the comments below this post.

# Le jeu de l'urne

Lors de la visite au laboratoire d'une brillante élève de seconde (salut Lena!), nous avons inventé ensemble un jeu: le jeu de l'urne. Le principe est simple: il faut deviner la couleur de la balle qu'on tire d'une urne contenant autant de balles rouges que noires - et ceci le plus tôt possible. Plus précisément, les règles sont:

• On a un ensemble de balles, la motié sont rouges, l'autre moitié noires (c'est donc un nombre pair de balles qu'on appelera $N$, disons $N=8$).
• Elles sont dans une urne opaque et donc on ne peut pas les voir à moins de les tirer une par une (sans remise dans l'urne). On peut tirer autant de balles qu'on veut pour les observer.
• Le but est de deviner la balle qu'on va tirer. Si on gagne (on a bien prédit la couleur), alors on gagne autant de points que le nombre de balles qui étaient dans l'urne au moment de la décision. Sinon on perd autant de points que l'on en aurait gagné!
• à long terme, la stratégie du jeu est de décider le meilleur moment où on est prêt à deviner la couleur de la balle qu'on va prendre et ainsi de gagner le plus de points possibles.

Nous avons d'abord créé ce jeu grâce au language de programmation Scratch sur https://scratch.mit.edu/projects/165806365/:

Ici, nous allons essayer de l'analyser plus finement.

# testing COMPs-fastPcum_scripted

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). Compared to the previous post, we integrated the faster code to https://github.com/bicv/SHL_scripts.

This is joint work with Victor Boutin.

# testing COMPs-fastPcum

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). Compared to the previous post, we optimize the code to be faster.

This is joint work with Victor Boutin.

# testing COMPs-Pcum

In this notebook, we will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ). In particular, we will show how one can build the non-linear functions based on the activity of each filter and which implement homeostasis.

This is joint work with Victor Boutin.

# A change in the definition of spatial frequency bandwidth?

Since the beginning, we have used a definition of bandwidth in the spatial frequency domain which was quite standard (see supp material for instance):

$$\mathcal{E}(f; sf_0, B_{sf}) \propto \frac {1}{f} \cdot \exp \left( -.5 \frac {\log( \frac{f}{sf_0} ) ^2} {\log( 1 + \frac {B_sf}{sf_0} )^2 } \right)$$

This is implemented in the folowing code which reads:

env = 1./f_radius*np.exp(-.5*(np.log(f_radius/sf_0)**2)/(np.log((sf_0+B_sf)/sf_0)**2))


However the one implemented in the code looks different (thanks to Kiana for spotting this!), so that one can think that the code is using:

$$\mathcal{E}(f; sf_0, B_{sf}) \propto \frac {1}{f} \cdot \exp \left( -.5 \frac {\log( \frac{f}{sf_0} ) ^2} {\log(( 1 + \frac {B_sf}{sf_0} )^2 ) } \right)$$

The difference is minimal, yet very important for a correct definition of the bandwidth!

# Manipulating speed in motion clouds

An essential dimension of motion is speed. However, this notion is prone to confusions as the speed that has to be measured can be relative to different object. Is it the speed of pixels? The speed of visual objects? We try to distinguish the latter two in this post.

In [1]:
%matplotlib inline
import numpy as np
np.set_printoptions(precision=3, suppress=True)
import pylab
import matplotlib.pyplot as plt

In [2]:
import os
name = '2017-04-10_physical_speed'
DOWNSCALE = 1

import MotionClouds as mc
N_X, N_Y, N_frame = mc.N_X/DOWNSCALE, mc.N_Y/DOWNSCALE, mc.N_frame/DOWNSCALE

fx, fy, ft = mc.get_grids(N_X, N_Y, N_frame)
mc.figpath = '../files/2017-04-10_physical_speed/'
if not(os.path.isdir(mc.figpath)): os.mkdir(mc.figpath)


In standard Motion Clouds, speed is that of the pixels of the textons that produce the texture. It is thus defined as a (normal) distribution of probability around the speed plane that defines the mean speed:

In [3]:
z = mc.envelope_gabor(fx, fy, ft)
name_ = name + '_vanilla'
mc.figures(z, name_, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)

/usr/local/lib/python3.7/site-packages/vispy/visuals/isocurve.py:22: UserWarning: VisPy is not yet compatible with matplotlib 2.2+
warnings.warn("VisPy is not yet compatible with matplotlib 2.2+")


### textures with motion parallax¶

Another way to consider speed is that of visual objects. Consider looking through the window of a train a field of similarly sized (Gabor-shaped) bushes on an infinite plane. The objects which are closer appear bigger on the retina and their speed is perceived as higher than the smaller bushes near the horizon. This relation between size and speed is linear and is called motion parallax (credit: http://www.rhsmpsychology.com/Handouts/monocular_cues_IV.htm).

In [4]:
from IPython.display import Image
Image('http://www.rhsmpsychology.com/images/monocular_IV.jpg')

Out[4]:

Such a pattern of motion distribution is highly prototypical in natural settings and it is easy to generate a texture having such a profile but with textons that are uniformly spaced in space, similar to the "Golconde" painting from Magritte (By The Shimon Yanowitz Website, Fair use, https://en.wikipedia.org/w/index.php?curid=3773027):

In [5]:
Image('https://upload.wikimedia.org/wikipedia/en/7/71/Golconde.jpg')

Out[5]:

More specifically, one finds that if we place MotionClouds on different planes perpendicular to the axis of vision, their frequency will increase as they get further , but their temporal frequency keeps constant: $$f_0 \propto \arctan(\frac{F_0}{D})$$ and $$f_t \propto F_T$$

where $F_0$ is the central scale (spatial frequency) of the visual object, $f_0$ its frequency in retinal space and $D$ the distance. Compared to the classical definition of MotionClouds, this affects only the way we define the enveloppe around the speed plane. For a parallax-compatible motion, this enveloppe is on a plane around a fixed temporal frequency:

In [6]:
mc.B_sf

Out[6]:
0.1
In [7]:
def envelope_parallax_speed(fx, fy, ft, K=1., V_X=mc.V_X, V_Y=mc.V_Y,
B_V=mc.B_V, sf_0=mc.sf_0, B_sf=mc.B_sf, loggabor=mc.loggabor,
theta=mc.theta, B_theta=mc.B_theta, alpha=mc.alpha):
"""
parallax speed envelope:
selects a gaussian perpendicualr to the plane corresponding to the speed (V_X, V_Y) with some thickness B_V

"""

envelope = mc.envelope_color(fx, fy, ft, alpha=alpha)
envelope *= mc.envelope_orientation(fx, fy, ft, theta=theta, B_theta=B_theta)
envelope *= mc.envelope_radial(fx, fy, ft, sf_0=sf_0, B_sf=B_sf, loggabor=loggabor)

V = np.sqrt(V_X**2 + V_Y**2)
envelope *= np.exp(-.5*((np.sign(fx) * ft + sf_0 * V)**2/(B_V*sf_0)**2))
# Hack to remove the other part of the envelope - use angle instead to allow for higher B_theta values
return envelope

name_ = name + '_parallax'
opts= dict(V_X=.5, sf_0=.2, B_V=.1, B_sf=.25)
z = envelope_parallax_speed(fx, fy, ft, **opts)
mc.figures(z, name_, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)


Let's explore what happens for different values of B_V:

In [8]:
opts_= dict(V_X=.5, sf_0=.2)
for B_V_ in np.logspace(-1, 1, 5, base=2)*opts['B_V']:
name_ = name + '_parallax_B_V_' + str(B_V_).replace('.', '_') #os.path.join(mc.figpath, )
z = envelope_parallax_speed(fx, fy, ft, B_V=B_V_, **opts_)
mc.figures(z, name_, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)


### group speed vs phase speed¶

More generally, this manipulation ---by changing the shape of the enveloppe--- changes the group vs phase speeds, as we did before with simulation of (earthly) gravitational waves. This can be done explictly by changing the shape of the enveloppe:

In [9]:
def envelope_physical_speed(fx, fy, ft, K=1., V_X=mc.V_X, V_Y=mc.V_Y,
B_V=mc.B_V, sf_0=mc.sf_0, B_sf=mc.B_sf, loggabor=mc.loggabor,
theta=mc.theta, B_theta=mc.B_theta, alpha=mc.alpha):
"""
physical speed envelope:
selects a gaussian perpendicualr to the plane corresponding to the speed (V_X, V_Y) with some thickness B_V

"""

envelope = mc.envelope_color(fx, fy, ft, alpha=alpha)
envelope *= mc.envelope_orientation(fx, fy, ft, theta=theta, B_theta=B_theta)
envelope *= mc.envelope_radial(fx, fy, ft, sf_0=sf_0, B_sf=B_sf, loggabor=loggabor)

envelope *= np.exp(-.5*((ft + sf_0 * V_X - (f_radius - sf_0) / K )**2/(B_V*sf_0)**2))
# Hack to remove the other part of the envelope - use angle instead to allow for higher B_theta values
envelope *= fx*ft > 0

return envelope

name_ = name + '_group_speed'
K = 1.
opts= dict(V_X=.5, sf_0=.2, B_V=.2)
z = envelope_physical_speed(fx, fy, ft, K=K, **opts)
mc.figures(z, name_)
mc.in_show_video(name_)


Let's explore what happens for different values of K:

In [10]:
for K_ in 1./np.linspace(0.6, 1.4, 7)*K:
name_ = name + '_group_speed_K_' + str(K_).replace('.', '_')
z = envelope_physical_speed(fx, fy, ft, K=K_, **opts)
mc.figures(z, name_)
mc.in_show_video(name_)


# Extending Olshausens classical SparseNet

• In a previous notebook, we tried to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse. In particular, we saw that in order to optimize competition, it is important to control cooperation and we implemented a heuristic to just do this.

• In this notebook, we provide an extension to the SparseNet algorithm. We will study how homeostasis (cooperation) may be an essential ingredient to this algorithm working on a winner-take-all basis (competition). This extension has been published as Perrinet, Neural Computation (2010) (see https://laurentperrinet.github.io/publication/perrinet-10-shl ):

@article{Perrinet10shl,
Title = {Role of homeostasis in learning sparse representations},
Author = {Perrinet, Laurent U.},
Journal = {Neural Computation},
Year = {2010},
Doi = {10.1162/neco.2010.05-08-795},
Keywords = {Neural population coding, Unsupervised learning, Statistics of natural images, Simple cell receptive fields, Sparse Hebbian Learning, Adaptive Matching Pursuit, Cooperative Homeostasis, Competition-Optimized Matching Pursuit},
Month = {July},
Number = {7},
Url = {https://laurentperrinet.github.io/publication/perrinet-10-shl},
Volume = {22},
}


This is joint work with Victor Boutin.

# Reproducing Olshausen's classical SparseNet (part 3)

In this notebook, we test the convergence of SparseNet as a function of different learning parameters. This shows the relative robustness of this method according to the coding parameters, but also the importance of homeostasis to obtain an efficient set of filters:

• first, whatever the learning rate, the convergence is not complete without homeostasis,
• second, we achieve better convergence for similar learning rates and on a certain range of learning rates for the homeostasis
• third, the smoothing parameter alpha_homeo has to be properly set to achieve a good convergence.
• last, this homeostatic rule works with the different variants of sparse coding.

This is joint work with Victor Boutin.

# Reproducing Olshausens classical SparseNet (part 2)

• In a previous notebook, we tried to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.

• However, the dictionaries are qualitatively not the same as the one from the original paper, and this is certainly due to the lack of control in the competition during the learning phase.

• Herein, we re-implement the cooperation mechanism in the dictionary learning routine - this will be then proposed to the main code.

This is joint work with Victor Boutin.

# Reproducing Olshausen's classical SparseNet (part 1)

• This notebook tries to reproduce the learning strategy specified in the framework of the SparseNet algorithm from Bruno Olshausen. It allows to efficiently code natural image patches by constraining the code to be sparse.

• the underlying machinery uses a similar dictionary learning as used in the image denoising example from sklearn and our aim here is to show that a novel ingredient is necessary to reproduce Olshausen's results.

• All these code bits is regrouped in the SHL scripts repository (where you will also find some older matlab code). You may install it using

    pip install git+https://github.com/bicv/SHL_scripts

# Bogacz (2017) A tutorial on free-energy

I enjoyed reading "A tutorial on the free-energy framework for modelling perception and learning" by Rafal Bogacz, which is freely available here. In particular, the author encourages to replicate the results in the paper. He is himself giving solutions in matlab, so I had to do the same in python all within a notebook...

# Resizing a bunch of files using the command-line interface

## generating databases¶

A set of bash code to resize images to a fixed size.

Problem statement: we have a set of images with heterogeneous sizes and we want to homogenize the database to avoid problems when classifying them. Solution: ImageMagick.

We first identify the size and type of images in the database. The database is a collection of folders containing each a collection of files. We thus do a nested recursive loop:

# Using generators in Python

Let's explore generators and the yield statement in the python language...

# Finding extremal values in a nd-array

Sometimes, you need to pick up the $N$-th extremal values in a mutli-dimensional matrix.

Let's suppose it is represented as a nd-array (here, I further suppose you are using the numpy library from the python language). Finding extremal values is easy with argmax, argmin or argsort but this function operated on 1d vectors... Juggling around indices is sometimes not such an easy task, but luckily, we have the unravel_index function.

For those in a hurry, one quick application is that given an np.ndarray, it's eeasy to get the index of the maximal value in that array :

In [1]:
import numpy as np
x = np.arange(2*3*4).reshape((4, 3, 2))
x = np.random.permutation(np.arange(2*3*4)).reshape((4, 3, 2))
print ('input ndarray', x)
idx = np.unravel_index(np.argmax(x.ravel()), x.shape)
print ('index of maximal value = ', idx, ' and we verify that ', x[idx], '=', x.max())

input ndarray [[[ 4  3]
[19  7]
[ 1 13]]

[[ 6 12]
[14  0]
[16 11]]

[[15 17]
[20 22]
[ 9  5]]

[[ 8 18]
[23 21]
[10  2]]]
index of maximal value =  (3, 1, 0)  and we verify that  23 = 23


Let's unwrap how we found such an easy solution...

# Saving and displaying movies and dynamic figures

It is insanely useful to create movies to illustrate a talk, blog post or just to include in a notebook:

In [1]:
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="../files/2016-11-15_noise.mp4" width=61.8%/></center>')

Out[1]:

For years I have used a custom made solution made around saving single frames and then calling ffmpeg to save that files to a movie file. That function (called anim_save had to be maintained accross different libraries to reflect new needs (going to WEBM and MP4 formats for instance). That made the code longer than necessary and had not its place in a scientific library.

Here, I show how to use the animation library from matplotlib to replace that

A new version of https://nteract.io/ is out, I will try today to push that new infomation to http://caskroom.io/ by creating a new cask for this application. I will base things on this previous contribution where I was simply editing an existing cask.

• getting the token

"$(brew --repository)/Library/Taps/caskroom/homebrew-cask/developer/bin/generate_cask_token" '/Applications/nteract.app'  • set-up variables cd "$(brew --prefix)"/Homebrew/Library/Taps/caskroom/homebrew-cask
github_user='laurentperrinet'
project='nteract'
git remote -v


# A pi-pie named Monte Carlo

A lively community of people including students, researchers and tinkerers from Marseille (France) celebrate the so-called "π-day" on the 3rd month, 14th day of each year. A nice occasion for general talks on mathematics and society in a lively athmosphere and of course to ... a pie contest!

I participated last year (in 2016) with a pie called "Monte Carlo". Herein, I give the recipe by giving some clues on its design... This page is a notebook - meaning that you can download it and re-run the analysis I do here at home (and most importantly comment or modify it and correct potential bugs...).

# Une tarte au pi nommée Monte Carlo

Une active communauté d'étudiants, chercheurs et bidouilleurs célèbrent à Marseille la "journée π" le 3ème mois, 14ème jour de chaque année. Une occasion de rêve pour en apprendre plus sur les mathématiques et la science dans une ambiance conviviale... Mais c'est aussi l'occasion d'un concours de tartes!

J'ai eu l'opportunité d'y participer l'an dernier (soit pour l'édition 2016) avec une tarte appelée "Monte Carlo". Je vais donner ici la "recette" de ma tarte, le lien avec le nombre π et quelques digressions mathématiques (notament par rapport à la présence incongrue d'un éléphant mais aussi par rapport à la démarche scientifique)... Cette page est un "notebook" - vous pouvez donc la télécharger et relancer les analyses et figures (en utilisant python + jupyter). C'est aussi un travail non figé - prière de me suggérer des corrections!

# Predictive coding of motion in an aperture

After reading the paper http://www.jneurosci.org/content/34/37/12601.full by Helena X. Wang, Elisha P. Merriam, Jeremy Freeman, and David J. Heeger (The Journal of Neuroscience, 10 September 2014, 34(37): 12601-12615; doi: 10.1523/JNEUROSCI.1034-14.2014), I was interested to test the hypothesis they raise in the discussion section :

The aperture-inward bias in V1–V3 may reflect spatial interactions between visual motion signals along the path of motion (Raemaekers et al., 2009; Schellekens et al., 2013). Neural responses might have been suppressed when the stimulus could be predicted from the responses of neighboring neurons nearer the location of motion origin, a form of predictive coding (Rao and Ballard, 1999; Lee and Mumford, 2003). Under this hypothesis, spatial interactions between neurons depend on both stimulus motion direction and the neuron's relative RF locations, but the neurons themselves need not be direction selective. Perhaps consistent with this hypothesis, psychophysical sensitivity is enhanced at locations further along the path of motion than at motion origin (van Doorn and Koenderink, 1984; Verghese et al., 1999).

Concerning the origins of aperture-inward bias, I want to test an alternative possibility. In some recent modeling work:

Laurent Perrinet, Guillaume S. Masson. Motion-based prediction is sufficient to solve the aperture problem. Neural Computation, 24(10):2726--50, 2012 https://laurentperrinet.github.io/publication/perrinet-12-pred

I was surprised to observe a similar behavior: the trailing edge was exhibiting a stronger activation (i. e. higher precision revealed by a lower variance in this probabilistic model) while I would have thought intuitively the leading edge would be more informative. In retrospect, it made sense in a motion-based prediction algorithm as information from the leading edge may propagate in more directions (135° for a 45° bar) than in the trailing edge (45°, that is a factor of 3 here). While we made this prediction we did not have any evidence for it.

In this script the predictive coding is done using the MotionParticles package and for a http://motionclouds.invibe.net/ within a disk aperture.

# Static motion clouds?

Motion Clouds were defined in the origin to define parameterized moving textures. The library in itself is also interesting in itself to generate a spatial texture. Herein I describe a solution to generate just one static frame.

# compiling notebooks into a report

For a master's project in computational neuroscience, we adopted a quite novel workflow to go all the steps from the learning of the small steps to the wrtiting of the final thesis. Though we were flexible in our method during the 6 months of this work, a simple workflow emerged that I describe here.

# Compiling and using pyNN + NEST + python3

PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.

# A bit more fun with gravity waves

## More fun with gravity waves¶

Motion Clouds were defined in the origin to provide a simple parameterization for textures. Thus we used a simple unimodal, normal distribution (on the log-radial frequency space to be more precise). But the larger set of Random Phase Textures may provide some interesting examples, some of them can even be fun! This is the case of this simulation of the waves you may observe on the surface on the ocean.

Main features of gravitational waves are:

1. longer waves travel faster (tsunami are fast and global, ripples are slow and local) - speed is linearly proportional to wavelength
2. phase speed (following a wave's crest) is twice as fast as group speed (following a group of waves).

# IRM clouds

A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the otirentation mean and bandwidth.

# Compiling and using pyNN + NEST + python3

PyNN is a neural simulation language which works well with the NEST simulator. Here I show my progress in using both with python 3 and how to show results in a notebook.

# Mirror clouds

An essential component of natural images is that they may contain order at large scales such as symmetries. Let's try to generate some textures with an axial (arbitrarily vertical) symmetry and take advantage of the different properties of random phase textures.

# Using scratch to illustrate the Flash-Lag Effect

Scratch (see https://scratch.mit.edu/) is a programming language aimed at introducing coding litteracy to schools and education. Yet you can implement even complex algorithms and games. It is visual, multi-platform and critically, open-source. Also, the web-site educates to sharing code and it is very easy to "fork" an existing project to change details or improve it. Openness at its best!

During a visit of a 14-year schoolboy at the lab, we used that to make a simple psychopysics experiment available at https://scratch.mit.edu/projects/92044597/ :

# élasticité trames V1

L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.

On va maintenant utiliser des forces elastiques pour coordonner la dynamique des lames dans la trame.

# bootstraping posts for élasticité

L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu.

.. media:: http://vimeo.com/150813922

Ce meta-post gère la publication sur https://laurentperrinet.github.io/sciblog.

# Reproducing Olshausen's classical SparseNet (part 3)

This is an old blog post, see the newer version in this post

# Reproducing Olshausen's classical SparseNet (part 4)

This is an old blog post, see the newer version in this post and following.

• A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/

• I will base things on this previous contribution

• set-up variables

cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='owncloud' git remote -v  Read more… # élasticité - scénario final montage L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post produit le montage final des séquences. Read more… # élasticité - scénario onde L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post crée des ondes se propageant sur la série de lames. Read more… # élasticité, geometrie L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post implémente une configuration favorisant des angles droits. Read more… # élasticité - scénario vague L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post implémente une configuration implémentant une vague de propagation. Read more… # élasticité expansion en miroir - dynamique d'un point focal L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post implémente une dynamique sur le point focal. Read more… # élasticité expansion en miroir - exploration paramètres L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post explore les paramètres de la structure et de l'extension des reflections. Read more… # élasticité expansion en miroir - principes L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post étudie quelques principes fondamentaux relatifs à des reflections mutliples dans des mirroirs. Read more… # Multiprocessing Testing some multi-threading libraries Read more… # élasticité expansion L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post étudie commment sampler des points sur la structure. Read more… # élasticité expansion-réaction diffusion L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post étudie une reaction de reaction diffusion sur la structure. Read more… # élasticité rapsberry pyserial L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post étudie la connection entre le raspberry π (qui fait tourner les simulations) et les arduino (qui commandent les moteurs). Read more… # élasticité, control scenario L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post simule une configuration de contrôle sur l'ensemble de la structure. Read more… # élasticité, Fresnel L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post simule une configuration de type Fresnel sur l'ensemble de la structure. Read more… # élasticité, vapory and reflections L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post simule un rendu de reflection utilisant povray. A scene with mirrors rendered with vapory (see https://laurentperrinet.github.io/sciblog/posts/2015-01-16-rendering-3d-scenes-in-python.html ) Read more… # Smooth transition between MCs A smooth transition of MotionClouds while smoothly changing their parameters. Read more… # A simpler e-mail client: Diving into mutt My installation notes for mutt_+Homebrew_+gmail, based on this post by steve losh and this other post Read more… # A hitchhiker guide to Matching Pursuit The Matching Pursuit algorithm is popular in signal processing and applies well to digital images. I have contributed a python implementation and we will show here how we may use that for extracting a sparse set of edges from an image. @inbook{Perrinet15bicv, title = {Sparse models}, author = {Perrinet, Laurent U.}, booktitle = {Biologically-inspired Computer Vision}, chapter = {13}, editor = {Keil, Matthias and Crist\'{o}bal, Gabriel and Perrinet, Laurent U.}, publisher = {Wiley, New-York}, year = {2015} }  Read more… # A simple pre-processing filter for image processing When processing images, it is useful to avoid artifacts, in particular when you try to understand biological processes. In the past, I have used natural images (found on internet, grabbed from holiday pictures, ...) without controlling for possible problems. In particular, digital pictures are taken on pixels which are most often placed on a rectangular grid. It means that if you rotate that image, you may lose information and distort it and thus get wrong results (even for the right algorithm!). Moreover, pictures have a border while natural scenes do not, unless you are looking at it through an aperture. Intuitively, this means that large objects would not fit on the screen and are less informative. In computer vision, it is easier to handle these problems in Fourier space. There, an image (that we suppose square for simplicity) is transformed in a matrix of coefficients of the same size as the image. If you rotate the image, the Fourier spectrum is also rotated. But as you rotate the image, the information that was in the corners of the original spectrum may span outside the spectrum of the rotated image. Also, the information in the center of the spectrum (around low frequencies) is less relevant than the rest. Here, we will try to keep as much information about the image as possible, while removing the artifacts related to the process of digitalizing the picture. Read more… # Extending Olshausens classical SparseNet This is an old blog post, see the newer version in this post Read more… # Reproducing Olshausens classical SparseNet (part 2) This is an old blog post, see the newer version in this post Read more… # Reproducing Olshausen's classical SparseNet (part 1) This is an old blog post, see the newer version in this post Read more… # trame-sensorielle L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. Ce post utilise une image naturelle comme entrée d'une "trame sensorielle". Read more… # Moving from Mayavi to VisPy A long standing dependency of MotionClouds is MayaVi. While powerful, it is tedious to compile and may discourage new users. We are trying here to show some attempts to do the same with the vispy library. In another post, we tried to use matplotlib, but this had some limitations. Let's now try vispy, a scientific visualisation library in python using opengl. Read more… # elastic-force L'installation Elasticité dynamique agit comme un filtre et génère de nouveaux espaces démultipliés, comme un empilement quasi infini d'horizons. Par principe de réflexion, la pièce absorbe l'image de l'environnement et accumule les points de vue ; le mouvement permanent requalifie continuellement ce qui est regardé et entendu. On va maintenant utiliser des forces elastiques pour coordonner la dynamique des lames dans la trame. Read more… # homebrew cask : updating an existing cask • A new version of psychopy is out, I will try today to push that new infomation to http://caskroom.io/ • I will base things on this previous contribution • set-up variables cd$(brew --prefix)/Library/Taps/caskroom/homebrew-cask
github_user='meduz'
project='psychopy'
git remote -v


# The Vancouver set

I have heard Vancouver can get foggy and cloudy in the winter. Here, I will provide some examples of realistic simulations of it...

This stimulation was used in the following poster presented at VSS:


@article{Kreyenmeier2016,
author = {Kreyenmeier, Philipp and Fooken, Jolande and Spering, Miriam},
doi = {10.1167/16.12.457},
issn = {1534-7362},
journal = {Journal of Vision},
month = {sep},
number = {12},
pages = {457},
publisher = {The Association for Research in Vision and Ophthalmology},
title = {{Similar effects of visual context dynamics on eye and hand movements}},
url = {http://jov.arvojournals.org/article.aspx?doi=10.1167/16.12.457},
volume = {16},
year = {2016}
}

• A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/

• I will base things on this previous contribution

• set-up variables

cd $(brew --prefix)/Library/Taps/caskroom/homebrew-cask github_user='meduz' project='owncloud' git remote -v  Read more… # elastic-grids-of-edges Dans un notebook précédent, on a vu comment créer une grille hexagonale et comment l'animer. On va maintenant utiliser MoviePy pour animer ces plots. Read more… # Saving files in HoloViews Here, we show how to save files from figures generated with HoloViews. This was thoroughly explained in this page. Read more… # An optic flow with Motion Clouds ## Horizon¶ Script done in collaboration with Jean Spezia. Read more… # Tiling Motion Clouds Script done in collaboration with Jean Spezia. Read more… # Creating an animation using Gizeh + MoviePy Gizeh (that is, Cairo for tourists) is a great interface to the Cairo drawing library. I recently wished to make a small animation of a bar moving in the visual field and crossing a simple receptive field to illustrate some simple motions that could be captured in the primary visual cortex ansd experiments that could be done there. Read more… # homebrew cask : updating an existing cask • A new version of owncloud is out, I will try today to push that new infomation to http://caskroom.io/ • I will base things on my contribution • set-up variables cd$(brew --prefix)/Library/Taps/caskroom/homebrew-cask
github_user='meduz'
project='owncloud'
git remote -v


# Rendering 3D scenes in python

The above snippet shows how you can create a 3D rendered scene in a few lines of codes (from http://zulko.github.io/blog/2014/11/13/things-you-can-do-with-python-and-pov-ray/):

In [1]:
import vapory

camera = vapory.Camera( 'location', [0, 2, -3], 'look_at', [0, 1, 2] )
light = vapory.LightSource( [2, 4, -3], 'color', [1, 1, 1] )
sphere = vapory.Sphere( [0, 1, 2], 2, vapory.Texture( vapory.Pigment( 'color', [1, 0, 1] )))

scene = vapory.Scene(camera = camera , # a Camera object
objects = [light, sphere], # POV-Ray objects (items, lights)
included = ["colors.inc"]) # headers that POV-Ray may need

# passing 'ipython' as argument at the end of an IPython Notebook cell
# will display the picture in the IPython notebook.
scene.render('ipython', width=900, height=500)

Out[1]:

Here are more details...

# The right imports in a notebook

Following this post http://carreau.github.io/posts/10-No-PyLab-Thanks.ipynb.html, here is ---all in one single cell--- the bits necessary to import most useful libraries in an ipython notebook:

In [1]:
# import numpy and set the printed precision to something humans can read
import numpy as np
np.set_printoptions(precision=2, suppress=True)
# set some prefs for matplotlib
import matplotlib.pyplot as plt
import matplotlib
matplotlib.rcParams.update({'text.usetex': True})
fig_width_pt = 700.  # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27               # Convert pt to inches
fig_width = fig_width_pt*inches_per_pt  # width in inches
FORMATS = ['pdf', 'eps']
phi = .5*np.sqrt(5) + .5 # useful ratio for figures
# define plots to be inserted interactively
%matplotlib inline
#%config InlineBackend.figure_format='retina' # high-def PNGs, quite bad when using file versioning
%config InlineBackend.figure_format='svg'


Below, I detail some thoughts on why it is a perfect preamble for most ipython notebooks.

# todo.txt

In a previous post, I described steps to follow to live with decentralized, open-source cloud services (see here), let's focus on setting up a todo list.

Everything revolves under the http://todotxt.com/ specifications -- all in one, simple todo.txt file

# Bib Cloud

I always used a single babel.bib BibTex file to keep a record of all my readings. Great. Then I managed it using a VCS (first SVN, then git). Super great. But at the same time, resources like CiteUlike or Mendeley (but also other services like orcid) allow for cloud-like services of having this data everywhere, anytime. Super super great!

But this fails if you have no connection to internet (remote conference, ...) or more importantly if these services change their policy (mendeley to citeulike sync disappeared at a sudden). The work you provide is not yours. These are mostly commercial services while all the open-source tools are there. Most importantly, the multiplicity of tools makes it difficult to share bibliographic data easily, while if we would have a tool to translate between them this would make it possible (without changing your habits).

# Can we live without Google and co?

The tools offered by so-called "cloud services" are useful but most often rely on the capacity to lock you in. Without leaving them completely, is there an alternative?

In a previous post on how to live with open-source cloud services and to not depend on centralized private cloud services (see here), let's focus on an install on a empty android phone.

# Recruiting different population ratios in V1 using orientation components: defining a protocol

A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the otirentation mean and bandwidth.

This is part of a larger study to tune orientation bandwidth.

# Polar bar plots

I needed to show prior information for the orientation of contours in natural images showing a preference for cardinal axis. A polar plot showing seemed to be a natural choice for showing the probability distribution function. However, this seems visually flawed...

# Can we live without Google and co?

The tools offered by so-called "cloud services" are useful but most often there is a (natural) tendency for these services to make you use their tools (youtube for google, iphone for apple). Why do we need to be locked to these services when open-source alternatives abound?

# paramétrer l'e-mail à l'INT

• paramétrer un nouveau compte avec son adresse lolo.toto@univ-amu.fr
• indiquer les parametres:
1. en entrant, serveur IMAP imap.univ-amu.fr avec SSL (port 993) et l'authentification est par mot de passe
2. en sortant, serveur SMTP smtp.univ-amu.fr en STARTTLS, port 587 et l'authentification est par mot de passe
3. attention, en entrant et en sortant, comme identifiant, l'identifiant univ-amu (de la forme toto.l)

# Reading KML My Tracks files in ipython

It's easy to record tracks (for instance while running) using the "My tracks" app on android systems. What about being able to re-use them?

In :doc:2014-11-22-reading-kml-my-tracks-files-in-ipython we reviewed different approches. Let's now try to use this data.

# Reading KML My Tracks files in ipython

It's easy to record tracks (for instance while running) using the "My tracks" app (see https://play.google.com/store/apps/details?id=com.google.android.maps.mytracks) on android systems. What about being able to re-use them?

Here, I am reviewing some methods to read a KML file, including fastkml, pykml, to finally opt for a custom method with the xmltodict package.

# Reverse-phi and Asymmetry of ON and OFF responses

We test Reverse-phi motion and the Asymmetry of ON and OFF responses using MotionClouds.

# Recruiting different population ratios in V1 using orientation components

A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the orientation mean and bandwidth.

# Recruiting different population ratios in V1 using orientation components: a biphoton study

A feature of MotionClouds is the ability to precisely tune the precision of information following the principal axes. One which is particularly relevant for the primary visual cortical area of primates (area V1) is to tune the otirentation mean and bandwidth.

To install the necessary libraries, check out the documentation.

### summary of the biphoton protocol¶

For the biphoton experiment:

• The refresh rate of the screen is 70Hz and stimulation for 5 times 1s, which makes 350 images.
• for the spatial frequency 0.125 cyc/deg is optimal (between 0.01 and 0.16).
• for the temporal frequency 2 cyc/sec is optimal (between 0.8 and 4 sic/sec), we manipulate $B_V$ to get a qualitative estimate.

# A bit of fun with gravity waves

## A bit of fun with gravity waves¶

Motion Clouds were defined in the origin to provide a simple parameterization for textures. Thus we used a simple unimodal, normal distribution (on the log-radial frequency space to be more precise). But the larger set of Random Phase Textures may provide some interesting examples, some of them can even be fun! This is the case of this simulation of the waves you may observe on the surface on the ocean.

In [1]:
from IPython.display import HTML
HTML('<center><video controls autoplay loop src="../files/2014-10-24_waves/waves.mp4" width=61.8%/></center>')

Out[1]:

Main features of gravitational waves are:

1. longer waves travel faster (tsunami are fast and global, ripples are slow and local) - speed is linearly proportional to wavelength
2. phase speed (following a wave's crest) is twice as fast as group speed (following a group of waves).

# Statistics of time-varying images

In this notebook, we are interested in replicating the results from Dong and Attick (1995).

# Motion Plaid

## MotionPaids : from MotionClouds components to a plaid-like stimulus¶

In [1]:
import numpy as np
import MotionClouds as mc
downscale = 2
fx, fy, ft = mc.get_grids(mc.N_X/downscale, mc.N_Y/downscale, mc.N_frame/downscale)
name = 'MotionPlaid'

mc.figpath = '../files/2014-10-20_MotionPlaids'
import os
if not(os.path.isdir(mc.figpath)): os.mkdir(mc.figpath)


Plaids are usually created by adding two moving gratings (the components) to form a new simulus (the pattern). Such stimuli are crucial to understand how for instance information about motion is represented and processed and its neural mechanisms have been extensively studied notably by Anthony Movshon at the NYU. One shortcomming is the fact that these are created by components which created interference patterns when they are added (as in a Moiré pattern). The question remains to know if everything that we know about components vs pattern processing comes from these interference patterns or reaaly by the processing of the two componenents as a whole. As such, Motion Clouds are ideal candidates because they are generated with random phases: by construction, there should not be any localized interference between components. Let's verfy that:

Defintion of parameters:

In [2]:
theta1, theta2, B_theta = np.pi/4., -np.pi/4., np.pi/32


This figure shows how one can create MotionCloud stimuli that specifically target component and pattern cell. We show in the different lines of this table respectively: Top) one motion cloud component (with a strong selectivity toward the orientation perpendicular to direction) heading in the upper diagonal Middle) a similar motion cloud component following the lower diagonal Bottom) the addition of both components: perceptually, the horizontal direction is predominant.

In [3]:
print( mc.in_show_video.__doc__)


Columns represent isometric projections of a cube. The left column displays
iso-surfaces of the spectral envelope by displaying enclosing volumes at 5
different energy values with respect to the peak amplitude of the Fourier spectrum.
The middle column shows an isometric view of the faces of the movie cube.
The first frame of the movie lies on the x-y plane, the x-t plane lies on the
top face and motion direction is seen as diagonal lines on this face (vertical
motion is similarly see in the y-t face). The third column displays the actual
movie as an animation.

Given a name, displays the figures corresponding to the Fourier spectra, the
stimulus cubes and movies within the notebook.



Component one:

In [ ]:
diag1 = mc.envelope_gabor(fx, fy, ft, theta=theta1, V_X=np.cos(theta1), V_Y=np.sin(theta1), B_theta=B_theta)
name_ = name + '_comp1'
mc.figures(diag1, name_, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)

/usr/local/lib/python3.7/site-packages/vispy/visuals/isocurve.py:22: UserWarning: VisPy is not yet compatible with matplotlib 2.2+
warnings.warn("VisPy is not yet compatible with matplotlib 2.2+")


Component two:

In [ ]:
diag2 = mc.envelope_gabor(fx, fy, ft, theta=theta2, V_X=np.cos(theta2), V_Y=np.sin(theta2), B_theta=B_theta)
name_ = name + '_comp2'
mc.figures(diag2, name_, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)


The pattern is the sum of the two components:

In [ ]:
name_ = name
mc.figures(diag1 + diag2, name, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)


### A grid of plaids shows switch from transparency to pattern motion¶

Script done in collaboration with Jean Spezia.

In [ ]:
import os
import numpy as np
import MotionClouds as mc

name = 'MotionPlaid_9x9'
if mc.check_if_anim_exist(name):
B_theta = np.pi/32
N_orient = 9
downscale = 4
fx, fy, ft = mc.get_grids(mc.N_X//downscale, mc.N_Y//downscale, mc.N_frame//downscale)
mov = mc.np.zeros(((N_orient*mc.N_X//downscale), (N_orient*mc.N_X//downscale), mc.N_frame//downscale))
i = 0
j = 0
for theta1 in np.linspace(0, np.pi/2, N_orient)[::-1]:
for theta2 in np.linspace(0, np.pi/2, N_orient):
diag1 = mc.envelope_gabor(fx, fy, ft, theta=theta1, V_X=np.cos(theta1), V_Y=np.sin(theta1), B_theta=B_theta)
diag2 = mc.envelope_gabor(fx, fy, ft, theta=theta2, V_X=np.cos(theta2), V_Y=np.sin(theta2), B_theta=B_theta)
mov[(i)*mc.N_X//downscale:(i+1)*mc.N_X//downscale, (j)*mc.N_Y//downscale : (j+1)*mc.N_Y//downscale, :] = mc.random_cloud(diag1 + diag2, seed=1234)
j += 1
j = 0
i += 1
mc.anim_save(mc.rectif(mov, contrast=.99), os.path.join(mc.figpath, name))

In [ ]:
mc.in_show_video(name, figpath=mc.figpath)


As in (Rust, 06) we show in this table the concatenation of a table of 9x9 MotionPlaids where the angle of the components vary on respectively the horizontal and vertical axes.
The diagonal from the bottom left to the top right corners show the addition of two component MotionClouds of similar direction: They are therefore also intance of the same Motion Clouds and thus consist in a single component.
As one gets further away from this diagonal, the angle between both component increases, as can be seen in the figure below. Note that first and last column are different instance of similar MotionClouds, just as first and last lines in in the table.

### A parametric exploration on MotionPlaids¶

In [ ]:
N_orient = 8
downscale = 2
fx, fy, ft = mc.get_grids(mc.N_X/downscale, mc.N_Y/downscale, mc.N_frame)
theta = 0
for dtheta in np.linspace(0, np.pi/2, N_orient):
name_ = name + 'dtheta_' + str(dtheta).replace('.', '_')
diag1 = mc.envelope_gabor(fx, fy, ft, theta=theta + dtheta, V_X=np.cos(theta + dtheta), V_Y=np.sin(theta + dtheta), B_theta=B_theta)
diag2 = mc.envelope_gabor(fx, fy, ft, theta=theta - dtheta, V_X=np.cos(theta - dtheta), V_Y=np.sin(theta - dtheta), B_theta=B_theta)
mc.figures(diag1 + diag2, name_, seed=12234565, figpath=mc.figpath)
mc.in_show_video(name_, figpath=mc.figpath)


For clarity, we display MotionPlaids as the angle between both component increases from 0 to pi/2.
Left column displays iso-surfaces of the spectral envelope by displaying enclosing volumes at 5 different energy values with respect to the peak amplitude of the Fourier spectrum.
Right column of the table displays the actual movie as an animation.

# Animation in a notebook using holoviews

An easy way to include movie in a notebook using Holoviews.

# How can I display an image in the terminal?

Is there any sort of utility I can use to convert an image to ASCII and then print it in my terminal? I looked for one but couldn't seem to find any.



# Setting options in holoviews

### using holoviews : setting display options¶

Holoviews is a nice framework to extend pyplot capacities.

# 2014-10-08_Moving from Mayavi to Matplotlib

A long standing dependency of MotionClouds is MayaVi. While powerful, it is tedious to compile and may discourage new users. We are trying here to show some attempts to do the same with matplotlib or any other library.

# transferring (lots of) files to a remote server + saving space

I have recently been asked how to transfer lots of file from a backup server to a local disk. The context is that

1. the backup is an archive to be put on an external hard drive and then in a closet for future use,
2. the transfer line is not quite reliable and transfers may stop (for instance if it is mounted as a samba / windows / applefile share)

# grille-hexagonale

### Positionnement des lames¶

on définit des configurations pour la position des lames

# [Stage M1] Chloé Pasturel : week 6

Nous allons simuler un ring avec en entrée une courbe représentant des stimuli "naturels"

• simulations center-surround
• synthèse
• écriture poster en latex

# [Stage M1] Chloé Pasturel : week 5

### Loi de Von mises :¶

La densité est définie sur $[0, 2\pi[$ par $f(x) = \frac {e^{\kappa cos(\theta - m)}}{2 \pi I_{0}(\kappa)} ~$
avec $~ \kappa = \frac {1}{\sigma^{2}}$

L'orientation est définie sur $[0, \pi[$ par un remplaçant de la variable $~ (\theta_d - m) ~$ par $~ 2(\theta_o - m)$

nous obtenons alors la densité pour $\theta_o$: $f(x) = \frac{1}{2 \pi I_{0}(\kappa)} \cdot {e^{\frac{cos(2(\theta - m))}{\sigma^{2}}}}$ et $~ p = \frac {1} {2\pi \sigma_1 \sigma_2} \cdot e^{- 2 \frac{(m_2-m_1)^{2}}{\sigma_{1}^{2} + \sigma_{2}^{2}}}$

car avec le changement de variable : $\varepsilon(x) = \frac {1} {\sigma_{1} \sqrt{2\pi}} \cdot e^{\frac {-(2(x-m_{1}))^{2}} {2\sigma_{1}^{2}}}$ et $\gamma (x) = \frac {1} {\sigma_2 \sqrt{2\pi}} \cdot e^{\frac {-(2(x-m_2))^{2}} {2\sigma_{2}^{2}}}$

donc $\varepsilon(x) \cdot \gamma(x) = \frac {1}{2\pi \sigma_{1} \sigma_{2}} \cdot e^{-2(\frac {(x-m_{1})^{2}}{\sigma_{1}^{2}} + \frac{(x-m_{2})^{2}}{\sigma_{2}^{2}})} = \frac {1} {2\pi \sigma_1 \sigma_2} \cdot e^{- 2 \frac{(m_2-m_1)^{2}}{\sigma_{1}^{2} + \sigma_{2}^{2}}}$

# [Stage M1] Chloé Pasturel : week 4

• simulations avec différents MCs
• ring avec représentation de la vitesse angulaire

# [Stage M1] Chloé Pasturel : week 3

Travail sur les Motion Clouds et observation des différents changement de paramètre particulièrement B_sf

• introduction aux motions clouds: installation et display dans un notebook
• synthèse de clouds avec différents theta
• synthèse avec différents B_theta (V=0)
• utilisation du trick dans http://motionclouds.invibe.net/posts/smooth-transition-between-mcs.html pour créer un stimulus pour lequel l'orientation tourne de 0 à 2*pi
• proposer une façon simple de passer de ce signal à une entré pour le ring (un peu de maths? un MC est une texture définie en fourier, une cellule simple fait une convolution dans l'espace, donc une multiplication dans Fourier: on pourrait avoir la sortie linéaire du neurone de V1 directement ...)

http://neuralensemble.github.io/MotionClouds/

https://invibe.net/LaurentPerrinet/TagMotionClouds

# [Stage M1] Chloé Pasturel : week 2

Simulation d'un ring model grâce au programme brian et observation des conséquences du changement de différents paramêtres

• simulation d'un ring par l'intermédiaire de brian http://briansimulator.org/
• courbe de sélectivité en fonction des parametres
• courbe de sélectivité à différents contrastes

# [Stage M1] Chloé Pasturel : plan du stage

## Modele d'une entrée naturelle à un modèle de selectivité à l'orientation¶

Stage M1 de Chloé Pasturel, encadrée par Laurent Perrinet (INT, CNRS)

### Contexte du stage¶

(importé depuis https://invibe.net/LaurentPerrinet/TagAnrBalaV1 )

# [Stage M1] Chloé Pasturel : week 1

• définition du sujet - lecture Hansel
• mise en place du python notebook
• demos de brian

### installation des outils¶

1. virtual box
2. VM ubuntu
3. <<< guest additions + clone
4. neurodebian
    wget -O- http://neuro.debian.net/lists/trusty.de-md.full | sudo tee /etc/apt/sources.list.d/neurodebian.sources.list
sudo apt-key adv --recv-keys --keyserver pgp.mit.edu 2649A5A9
sudo apt-get update
1. install on ubuntu
sudo apt-get install aptitude
sudo aptitude install ipython-notebook
sudo aptitude install python-matplotlib
sudo aptitude install python-brian
sudo aptitude install texlive-full

sudo aptitude install python-pynn
pip install --user neurotools

# Vim commands for moving around

## Basics commands in Vim

Sometimes, it's good to go back to the basics.

In command mode, typing :help usr_02.txt (or simplier something like :h usr_<TAB>02<TAB><ENTER>), you learn the letters for navigating a file:

• these letters are HJKL - glad it works on an international keyboard.
• letters on the borders (HL) are for horizontal movements- obviously H for left, L for right
• letters on the inside are for vertical movements - J for down, K for up; a nice feature is that these keys are now quite widely used in the community, take for example in the gmail interface when switching to the next message.

I was still using the arrows keys, but taking this habit makes thinks easier, especially when switching often keyboards.

Simalarly, to scroll the text - you can use:

• <CTRL-U> to scroll a half-page up
• <CTRL-D> to scroll a half-page down

Here, the :h ctrl-u page will give you more info (or :help usr_03.txt).

Note that to follow a link (think "searching a tag"), you can press * (or # to go backwards).

# Batch removing files on a FTP server

In a previous post, I have shown how to convert MoinMoin pages to this blog (Nikola angine). Let's now tidy up the place and remove obsolete pages.

Pages that were succesfully converted:

# Converting moinmoin pages to nikola

nikolFrom a previous post, we have this function to import a page from moinmoin, convert it and publish:

# Converting MoinMoin pages to Nikola

I have another wiki to take notes. These are written using the MoinMoin syntax, which is nice, but I found no converter from MoinMoin to anything compatible with Nikola.

Following this post http://carreau.github.io/posts/06-NBconvert-Doc-Draft.html , I thought I might give it a try using ipython within nikola:

In [1]:
URL = 'https://invibe.net/cgi-bin/index.cgi/SciBlog/2005-10-29?action=print'


NOTE: this should not be tried with these URLS as they do not exist anymore...

# SSH says 'Read from socket failed: Operation timed out'

While SSH is rock solid, we stumbled on a strange bug while trying to establish a connection:



# Finder Shortcuts in MacOsX

• ⇧⌘C: Computer
• ⇧⌘H: Home
• ⇧⌘D: Desktop
• ⇧⌘K: Network
• ⇧⌘A: Applications
• ⇧⌘O: Documents
• ⇧⌘U: Utilities

# inverting colors in MacOsX

• during a presentation, a figure may reveal more readable if you invert colors and luminance: black gets white and reverse, red becomes cyan and so on.

# ubuntu : starting sshd at boot

• ssh server installed but not starting at boot (I certainly messed up something):

$ls -l /etc/init.d/*ssh* -rwxr-xr-x 1 root root 3704 2010-09-14 19:20 /etc/init.d/ssh$ ls -l /etc/rc2.d/*ssh*
ls: cannot access /etc/rc2.d/*ssh*: No such file or directory
\$ ls -l /etc/rc1.d/*ssh*


# how to find stuff

• the most simple command is locate :

locate Python.h


; it is based on a database updated regulalry (most often daily).

# Caps Lock, what a useless key

1. [Ubuntu / gnome] You should be disable it do this with System->Preferences->Keyboard->Layouts->Options...->CapsLockkeybehavior
2. [MacosX] Open SystemPreferences, select the Keyboard pane. Within here, click the ModifierKeys… button at the bottom. To disable the Caps Lock key, pull down the associated menu and select NoAction.

# CRAAC: Compte rendu annuel d'activité des chercheurs du CNRS. Année 2010

2010-12-08 10:56:10

Compte rendu annuel d'activité des chercheurs du CNRS Année 2009 - 2010

# ignoring a folder in SVN

• simply issue

svn propset svn:ignore '*' data/