Pascal Helson
About me
I am currently a post doctoral researcher at KTH Royal Institute of Technology in Stockholm within the NeuroLogic group of Arvind Kumar.
My academic career were guided by the ambition to do research in mathematics applied to biology. First, I had the opportunity to go to a Maths and Physics preparatory school at Lycée Masséna in Nice, France. Then, I went to the engineering school of Mines de Saint Etienne where I could learn more about Biology, Mathematics and Computational languages. Finally, I went to École des Ponts et Chaussées where I did the Master ANEDP of University Paris 6 (UPMC).
I did my PhD at Inria Sophia-Antipolis in the TOSCA and MathNeuro teams under the supervision of Etienne Tanré and Romain Veltz. My PhD focuses on modelling and analysing (mathematically and numerically) neural networks in which neurons interact through plastic synaptic weights. Analysing rigorously the coupled dynamics (of neurons and synapses) is a non trivial task. In order to overcome this difficulty, I use probabilistic tools to simplify such complex models : at the mesoscopic scale, I make a time-scale separation when there is some biological time-scale differences (synaptic plasticity much slower than the neurons dynamics for example) and at the macroscopic scale, I did a mean field analysis when the network considered possesses a large number of neurons.
My post doctoral research project is part of the dBRAIN project and under the supervision of Arvind Kumar. I am doing whole brain macroscopic modelling using imaging data in order to get more insight into Parkinson and Alzheimer diseases.
Mail : pashel@kth.se
Thesis : Plasticity in networks of spiking neurons in interaction, defended (slides in French) on March 29th, 2021.
4. Structural constraints on the emergence of oscillations in multi-population neural networks (with Jie Zang and Arvind Kumar)
(February 2023). arXiv
3. Cortex-wide topography of 1/f-exponent in Parkinson’s disease
npj Parkinson's Disease (July 2023). arXiv
2. A Mathematical Analysis of Memory Lifetime in a Simple Network Model of Memory
Neural Computation (July 2020). arXiv, HAL
1. A new STDP Rule in a neural Network Model (with Etienne Tanré and Romain Veltz)
Talks
INRIA Sophia-Antipolis PhD Seminar (2017) : Spike-Timing Dependent Plasticity (STDP) models or how to understand memory
CNOD 2017 : A simple spiking neuron model based on stochastic STDP
Second year PhD Colloquium EDSFA (2018) : A Mathematical approach on memory capacity of simple synapses models
Neuromod Seminar (2020) : A Mathematical approach on memory capacity of simple synapses models
CCNSv2 (2021) : A new stochastic STDP Rule in a neural Network Model
ICMNS (2021) : Slow-fast and long time behaviour analysis of a neural network with stochastic STDP
GdR ISIS meeting (2022) : Estimating the brain-wide distribution of excitation-inhibition balance in Parkinson's disease
Dive deep - Digital Futures (2023): Cortex-wide topography of 1/f-exponent in Parkinson’s disease
Posters
ICMNS 2017 : A simple spiking neuron model based on stochastic STDP
ICMNS 2018 : A mathematical approach on memory capacity of a simple synapses model
ICMNS 2019 : A Mathematical Analysis of Memory Lifetime in a simple Network model of Memory
MEG NORD 2023 : Cortex-wide excitation/inhibition topography in Parkinson’s disease
Workshops (organiser)
BrainNet 2023: video recordings on youtube !
Interpretable Brain Data (IBD) 2023: video recordings on youtube!