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Annonce

6 janvier 2017

Beating Roger Federer: Modeling visual learning and expertise through a bioinspired neural network embedded in an electronic device


Catégorie : Stagiaire


Goal: Internship for student in engineering school / Master degree’s student. The project can lead to a PhD grant.

When: 5-6 months from February to July 2017

Topic: How do expert tennis players, like Roger Federer for example, predict if a ball will bounce in or out the field to decide if it should be played or not? After thousands of trajectory presentations, best champions have developed extraordinary skills in such a task, but little is known on how the visual system turns selective to spatiotemporal properties of the visual stimulus (e.g., 3D position, velocity and acceleration) and learns how to make an efficient use of it.

The goal of the project is to build an embedded system – based on FPGA circuits and ARM processor – an artificial neural network which would replicate – and perhaps beat – the visual and anticipatory performances of these expert players.

To achieve this goal successfully, we will develop a bio-inspired neural network, based on some of the key properties of human vision: the Smart NeuroCam (GST company) will be used to reproduce the retina functioning. It triggers its message under the form of spikes, in an asynchronous way (without any concept of frame per second), responding to spatial or temporal changes in the pattern of illumination. Several kinds of pre-processing filters can be implemented in VHDL language directly in the FPGA circuits, and the output is then sent to a neural network. The artificial network will learn to use this message, applying a simple learning rule, the Spike-Timing-Dependent Plasticity (STDP). This rule allows each neuron to become selective to a particular property of the stimulus, completely autonomously and with no supervision. Several layers will be built to allow perceiving more and more complex properties of the visual scene. Once the network will be established, its performances will be assessed in different conditions of learning and compared to those of the best tennis players.

  • Robin Baurès, PhD
    Associate Professor
    CerCo, Université Toulouse 3, CNRS
    CHU Purpan, Pavillon Baudot
    31059 Toulouse Cedex 9 – France
    Office phone: 0033 (0)5 62 74 62 15
    Email : robin.baures@cnrs.fr
  • Pr Michel Paindavoine
    GlobalSensing Technologies
    14, rue Pierre de Coubertin
    21000 Dijon
    email : michel.paindavoine@gsensing.eu

Beating Roger Federer:

Modeling visual learning and expertise through a bioinspired neural network embedded in an electronic device

Goal: Internship for student in engineering school / Master degree’s student. The project can lead to a PhD grant.

When: 5-6 months from February to July 2017

Topic

How do expert tennis players, like Roger Federer for example, predict if a ball will bounce in or out the field to decide if it should be played or not? After thousands of trajectory presentations, best champions have developed extraordinary skills in such a task, but little is known on how the visual system turns selective to spatiotemporal properties of the visual stimulus (e.g., 3D position, velocity and acceleration) and learns how to make an efficient use of it.

The goal of the project is to build an embedded system – based on FPGA circuits and ARM processor – an artificial neural network which would replicate – and perhaps beat – the visual and anticipatory performances of these expert players.

To achieve this goal successfully, we will develop a bio-inspired neural network, based on some of the key properties of human vision: the Smart NeuroCam (GST company) will be used to reproduce the retina functioning. It triggers its message under the form of spikes, in an asynchronous way (without any concept of frame per second), responding to spatial or temporal changes in the pattern of illumination. Several kinds of pre-processing filters can be implemented in VHDL language directly in the FPGA circuits, and the output is then sent to a neural network. The artificial network will learn to use this message, applying a simple learning rule, the Spike-Timing-Dependent Plasticity (STDP). This rule allows each neuron to become selective to a particular property of the stimulus, completely autonomously and with no supervision. Several layers will be built to allow perceiving more and more complex properties of the visual scene. Once the network will be established, its performances will be assessed in different conditions of learning and compared to those of the best tennis players.

The project is funded by a French National Research Agency (ANR), involving two sites and several researchers:

Where: The candidate will be based at CerCo, Toulouse (France), and will make the interface with the two sites, with regular trips. The computational neuroscience part will be done at Toulouse, and electronic part at Dijon.

Objectives for the engineering / Master internship

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