Welcome to my website,

I'm Clementine Domine

Machine learning Neuroscience Physics Art


Let me introduce myself.

I am passionate and fascinated by the complex, flexible and optimized functioning of the brain that is yet left to be understood and characterized. I am particularly interested in studying the neural computational theories at the basis of information processing, learning and memory formation in neuronal networks. I am interested in answering questions such as: How is information from the environment represented by ensembles of neurons? How do the neural networks evolve with learning? How does the brain create, store, and update memories for places and events? I am looking forward to making advances in answering these questions, working at the intersection between theoretical neuroscience research and machine learning. It is my conviction that working on both artificial and biological neural networks in parallel will enable us to move forward faster in the general understanding of their mechanisms.

I am pursuing a Ph.D. program at The Gatsby Computational Neuroscience Unit under the supervision of Andrew SAXE and my ambition is to embrace an Academic research career in computational neuroscience. I have a master's degree in Theoretical Physics from the University of Manchester (UK).


  • Fullname: Clementine Carla Juliette Domine
  • Birth Date: September 07, 1998
  • Job: Phd in Computational neurosicence (UCL)/Theoretical physics master student / Data Scientist / musicianr
  • Website: ClementineDomine.github.io
  • Email: clementine.domine98@gmail.com


  • 90%
    Problem Solving
  • 90%
    Data Analysis
  • 75%
  • 90%
  • 80%
    Coding (C++/MATLAB/PYTHON)
  • 100%

Reasearch Experience

Prioritize Replay in Deep Neural Network

June - Present


In this work, we explore the dynamics of learning with prior unbedded knowledge. Within this framework, we aim at preventing catastrophic interference using prioritized replay. In particular, we use hierarchical data and a simple linear network to explore these ideas. This simple framework allowed us to derive the analytical solution of the autocorrelation of the weights during the revision task. Preliminary work seems to indicate interesting ways of prioritizing replay, which we will work on in the future.

GASTBY - Saxe Lab Collaboration

June - Present

Clementine Domine and Rodrigo Carrasco-Davis et al.

Prioritize A Standardised Environment for evaluation of Hippocampus and Entorhinal Cortex Models

This framework will allow neuroscientists to easily contrast the different hippocampus and entorhinal cortex models against experimental observations. This new tool aims at standardising the methods of building and comparing models as well as reporting empirical evidences. We create this framework with the perspective that the neuroscience community should be able to continue its development by increasing the databases of model and experimental results without our intervention. Therefore, the software is made easy to use, facilitating future growth. This project will develop the student's neuroscience knowledge and coding skills. Any significant contribution will be considered during the publication process. This new environment will revolutionize how the theoretical models are proposed in neuroscience and push for easy access and implementation of new ideas.

SWC- GASTBY Collaboration

April - Present

Clementine Domine, Rodrigo Carrasco-Davis, Will Dorrell, Tom George, Emmett Thompson, Lars Rollik, Georgina Mills, Marcus Stephenson-Jones

Multistep motor sequences in the Dorsolateral Striatum

Movement governs the ability of animals to interact with the environment and thus the experience of life itself. The brain’s complex neural circuits are responsible for the planning and learning of movements. Recent evidence from lesion experiments suggests that the Dorsolateral Striatum (DLS) plays a critical role in learning and executing motor sequences. Sleep is known to facilitate motor sequence learning but how sleep influences the neural dynamics in the DLS is yet to be found . For declarative memory, offline reactivation of awake patterns of activity during sleep (“replay”) is known to simulate experience and facilitate memory consolidation in hippocampus \cite{joo2018hippocampal}. Whether such a replay mechanism is used for procedural memory in DLS is unknown. Using a pattern recognition algorithm (PP-Seq), we aim to characterize neural data from DLS during awake behaviour and use it to show the presence of replay during sleep.


July - September 2019

Summer internship at the EPFL Blue Brain Project with Dr. Rajnish Ranjan, Section Manager of the Membrane Systems Group in the Simulation Neuroscience Division (Lausanne, Switzerland).

My main duties consist in redesigning and upgrading the existing ion channel model fitting with Hodgkin-Huxley and Markov model formulation.

Summer intern at CERN

July - September 2018/2017

Summer internship at CERN in the ALPHA Experiment

The ALPHA experiment is working with trapped anti-hydrogen atoms and aims at studying fundamental symmetries between matter and antimatter. I could develop my computing and electronic skills by working on a project around a “push-pull” Mosfet circuit for the apparatus.Further develop my computing and electronic skills working on a project of the electronic circuit on security valve of the cryostat for the future Alpha-G experiment which aims at measuring the gravity constant with antimatter. I Helped with the day-to-day running of the experiment. I Assisted Summer Student lectures. Certified guide inside the AD facility.


Teaching Assistant

Sept 2021 - June 2022

Teaching Assistant for the UCL courses


Theoretical Neurosciences


PhD Computational neurosciences

Sept 2020 - Present

University College London

I am pursuing a Ph.D. program at The Gatsby Computational Neuroscience Unit under the supervision of Andrew SAXE and my ambition is to embrace an Academic research career in computational neuroscience.

Master Degree in Theoretical Physics

Sept 2017 - April 2020

University of Manchester/ University of California Los Angeles

A particle-in-cell (PIC) computer code aiming at simulating the equilibrium conditions for an antiproton and electron plasma confined in a Penning-Malmberg trap was developed in C++ by Daniel Duque. This code is used for the study of the electron/antiproton separation technique used in the ALPHA experiment at CERN, called the e-kick procedure. In the first place, the antiprotons are cooled through collisions with electrons inside the trap which radiate the energy through cyclotron radiation. Removing the electrons is a crucial process that allows the cooled antiprotons to later recombine with positrons to produce antihydrogens. Different means of establishing appropriate initial conditions are investigated in this report. Various diagnostics were developed to study the e-kick process. The results of the side and center e-kick are reported and discussed with a focus on finding the minimum temperature of the antiproton at the end of the procedure.


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Phone: (+33)7 82 51 01 89


Check Out Some of My Work.

You will find here some media content and my favorite writting in English or in French

2D Particle-In-Cell (PIC) Computer Model for Antimatter Plasma Simulation (1)

Written in 2020/ (15/20min) Master Project - UOM .

2D Particle-In-Cell (PIC) Computer Model for Antimatter Plasma Simulation (2)

Written in 2019/ (15/20min) Master Project - UOM .

Non-Neutral Plasma simulation

Internship at EPFL Blue Brain Project

Written in 2020/ (15/20min) Report of Internship Blue Brain Project .

Advances in particle-accelerator technology

Written in 2017/ (10min) Summer Vacation Essay .