A PhD scholarship in mathematical and computational neuroscience on The modelling of excitatory synapses in healty / pathological condition is available at INRIA Sophia Antipolis, within the Team MathNeuro in collaboration with Dr Marie's team at the Institut de Pharmacologie Moléculaire et Cellulaire (IPMC, Sophia Antipolis).

This 3 year funded PhD scholarship starts in September 2017 or January 2018.

Candidate

The ideal candidate should have a background in nonlinear dynamics, stochastic analysis and computational neuroscience. He/she should also have strong ability to program in Python / C / Matlab / Julia. French is not a requirement if fluent in English, but willingness to learn would be beneficial.

Applications

Send CV, transcripts and contact informations of two persons we could reach for recommendation to romain.veltz@inria.fr and marie@ipmc.cnrs.fr.

Supervisors

Dr Romain Veltz (INRIA)
Dr Helene Marie (IPMC)

Project description

Synaptic plasticity is one of the fundamental phenomena which shape neural networks. It is thought to be the basis of our memory and learning capabilities. Synaptic plasticity have been modelled recently [1,2,3] but at a phenomenological level. It is for example quite difficult to link these model internal variables to the synapse biophysics. Another issue is noise. Whereas in many models, noise is added as cherry on top, it is in fact internally generated given the small number of synaptic constituants [4].

The project will focus on the development of a stochastic model of the postsynaptic side of an excitatory synapse, in healthy / Alzheimer conditions, which describes the behaviour of the first biochemical molecules targeted upon arrival of Glutamate. More precisely, the project will first aim at modelling the early phase plasticity (LTP, LTD, STDP) in light of recent experimental data [5,6] with aim to take into account data provided by Dr Marie's lab concerning deficient synapses observed in Alzheimer’s disease like conditions.

The project will involve a mix of high performance scientific computation, nonlinear dynamics, stochastic analysis, and an enthusiasm for learning about plasticity mechanisms in general.

References

1. Clopath, Claudia, Lars Büsing, Eleni Vasilaki, et Wulfram Gerstner. « Connectivity reflects coding: a model of voltage-based STDP with homeostasis ». Nature Neuroscience 13, nᵒ 3 (mars 2010): 344‑52. doi:10.1038/nn.2479.
2. Graupner, Michael, et Nicolas Brunel. « Calcium-Based Plasticity Model Explains Sensitivity of Synaptic Changes to Spike Pattern, Rate, and Dendritic Location ». Proceedings of the National Academy of Sciences, 22 février 2012. doi:10.1073/pnas.1109359109.
3. Costa, Rui Ponte, Robert C. Froemke, P. Jesper Sjöström, et Mark CW van Rossum. « Unified Pre- and Postsynaptic Long-Term Plasticity Enables Reliable and Flexible Learning ». ELife 4 (26 août 2015): e09457. doi:10.7554/eLife.09457.
4. Ribrault, Claire, Ken Sekimoto, et Antoine Triller. « From the Stochasticity of Molecular Processes to the Variability of Synaptic Transmission ». Nature Reviews Neuroscience 12, nᵒ 7 (juillet 2011): 375‑87. doi:10.1038/nrn3025.
5. Fujii, Hajime, Masatoshi Inoue, Hiroyuki Okuno, Yoshikazu Sano, Sayaka Takemoto-Kimura, Kazuo Kitamura, Masanobu Kano, et Haruhiko Bito. « Nonlinear Decoding and Asymmetric Representation of Neuronal Input Information by CaMKIIα and Calcineurin ». Cell Reports 3, nᵒ 4 (25 avril 2013): 978‑87. doi:10.1016/j.celrep.2013.03.033.
6. Tigaret, Cezar M., Valeria Olivo, Josef H.L.P. Sadowski, Michael C. Ashby, et Jack R. Mellor. « Coordinated activation of distinct Ca2+ sources and metabotropic glutamate receptors encodes Hebbian synaptic plasticity ». Nature Communications 7 (13 janvier 2016): 10289. doi:10.1038/ncomms10289.