Connectionists: PhD position in neuromorphic engineering - Toulouse, France

[Timothée Masquelier]

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

Goal: PhD grant.
When: starting from September 2018 (date may be flexible to some extent)
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:
− Robin Baurès, Benoit Cottereau, Timothée Masquelier and Simon Thorpe,
CerCo, Toulouse.
− Michel Paindavoine, GST, Dijon.

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.

Tasks:
− Matlab (or Python) based simulations of numerical filters. These filters
will be applied to the image processing from which spikes are generated and
then sent to feed the neural network and STDP learning mechanism.
− VHDL coding to implement these numerical filters into the FPGA circuits
of the cameras
− C/C++ coding of the neural network and STDP mechanism that should work on
an embedded ARM processor system
− Experimental tests that will allow evaluating the performance of the
whole system, from spikes generation to visual properties learning of the
embedded system, to predict tennis ball’s trajectories

Required skills:
− Strong knowledge on electronic, and openness to computational
neurosciences
− Knowledge in signal-image processing, and artificial neural network
− Interest for multidisciplinary research
− Ability to turn smoothly autonomous, once the road has been set
− Ability to be at the interface of two scientific fields and two working
areas
− Programming with Matlab and/or Python for simulating the neural network
− Programming in VHDL language for FPGA circuits
− Programming in C/C++ language for porting the algorithms on ARM
microprocessors
− French is not a requirement if fluent in English, but willingness to
learn would be beneficial

Citizenship Eligibility: For security / defense reasons, an evaluation will
be made of the candidates before any possible acceptation.

Relevant publications for the project:
− Masquelier, T., Guyonneau, R. & Thorpe S.J. (2009). Competitive
STDP-Based Spike Pattern Learning. Neural Comput, 21(5),1259-1276.
− Masquelier, T. & Thorpe, S.J. (2007). Unsupervised learning of visual
features through spike timing dependent plasticity. PLoS Comput Biol,
3(2):e31.
− Cottereau, B.R., McKee, S.P. & Norcia, A.M. (2014). Dynamics and cortical
distribution of neural responses to 2D and 3D motion in human. Journal of
Neurophysiology 111(3), 533-543.
− SmartNeuroCam de GST : https://gsensing.eu/fr/category/sections/products

Contact:
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 at cnrs.fr

Pr Michel Paindavoine
GlobalSensing Technologies
14, rue Pierre de Coubertin
21000 Dijon
email : michel.paindavoine at gsensing.eu

-- 
Timothée Masquelier - timothee.masquelier at cnrs.fr -
http://cerco.ups-tlse.fr/~masquelier/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20180612/41f8f9a7/attachment.html>