2017-12-21 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 http://invibe.net/LaurentPerrinet/Publications/Perrinet12pred

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.

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2017-12-13 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="http://blog.invibe.net/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.

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2017-11-07 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 http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). 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.

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2017-10-25 Designing a A0 poster using matplotlib

Poster GDR Vision

This poster was presented in Lille at a vision workshop, check out http://invibe.net/LaurentPerrinet/Publications/Perrinet17gdr

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.

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2017-10-06 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.

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2017-09-20 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 tensorflow. This is work-in-progress, so that any suggestion is welcome (for instance on StackExchange!

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2017-06-15 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.

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2017-03-29 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 http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). Compared to the previous post, we integrated the faster code to https://github.com/bicv/SHL_scripts.

See also the other posts on unsupervised learning,

This is joint work with Victor Boutin.

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2017-03-29 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 http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). Compared to the previous post, we optimize the code to be faster.

See also the other posts on unsupervised learning,

This is joint work with Victor Boutin.

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2017-03-29 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 http://invibe.net/LaurentPerrinet/Publications/Perrinet10shl ). In particular, we will show how one can build the non-linear functions based on the activity of each filter and which implement homeostasis.

See also the other posts on unsupervised learning,

This is joint work with Victor Boutin.

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