PhD Thesis Offer

  • Multiscale brain rhythms under healthy and epileptic conditions: computational modeling insights for clinical applications

    Neural activity in the brain operates across multiple scales, encompassing both spatial and temporal dynamics. In patients with epilepsy, however, cognitive impairments are often linked to disruptions in these neural mechanisms, particularly through interictal epileptiform discharges (IEDs). This project aims to uncover new insights into the link between electrophysiology and attention deficits, one of the most prevalent cognitive impairments in patients with epilepsy, by exploring the role of IEDs. The PhD candidate will develop a comprehensive neocortical population model. The model will be validated on electrophysiological signals recorded in epileptic patients, and its dynamics will be studied to detail the mechanisms of multiple timescale interactions giving rise to healthy and pathological activity.

    The research project is at the interface between computational, cognitive, and clinical neurosciences. The candidate will preferably have some background in applied mathematics or computational neuroscience/systems biology. Programming skills in Python and knowledge of dynamical systems are required. Knowledge in cognitive neuroscience, electrophysiology and/or EEG analysis would be an asset. The PhD fellow will join the Cophy Team hosted at the Center for Neuroscience Research of Lyon (CRNL), France. The ideal start date is September 2025, with some flexibility.

    Candidates should send their CV, a motivation letter, contact information for 2-3 references and their master degree notes (if available) to Elif Köksal-Ersöz elif.koksal@inria.fr and Mathilde Bonnefond mathilde.bonnefond@inserm.fr until June 10th 2025.

    Related Publications:

    1. Bonnefond M, Kastner S, Jensen O. Communication between Brain Areas Based on Nested Oscillations. eNeuro. 2017; 4(2).
    2. Köksal-Ersöz E, Lazazzera R, Yochum M, Merlet I, Makhalova J, Mercadal B, et al. Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones. J Neural Eng. 2022;19(5):055005.
    3. Köksal-Ersöz E, Yochum M, Benquet P, Wendling F. eCOALIA: Neocorticalneural mass model for simulating electroencephalographic signal, SoftwareX. 2024, 28: 101924.
    4. Thieux M, Jung J. et al. BLAST paradigm. Epilepsy & Behavior. 2019; 99: 106470.

    Master Internship Offer 1

  • Role of interneurons in seizure control

    Seizures are intricate pathological events in neural networks, marked by excessive and synchronized neuronal activity, including a wide variety of inhibitory interneurons. Normally, inhibitory interneurons function to regulate brain excitation, but this system sometimes fails, leading to uncontrolled excitation. Recent advances in optogenetics, along with genetic tools, electrophysiological methods, and imaging techniques, have made it possible to examine the specific contributions of certain cell types to network dynamics. While these techniques have significantly enhanced our understanding of cortical circuits in epilepsy, the specific roles of different inhibitory cell types in seizure control are still not fully understood.. This internship will focus on the role of different types of inhibitory interneurons on seizure suppression. The objective is to model in vivo recordings with a recent neural mass model and to decipher the role of different types of interneurons during seizures and how their anti-epileptic action can be activated. Familiarity with dynamical systems, mathematical models, neurophysiology and proficiency with Python are required. The intern will join the Cophy Team and Tiger Team<\a> hosted at the Center for Neuroscience Research of Lyon (CRNL), France. The ideal start date is January 2026, with some flexibility.

    Candidates should send their CV and a motivation letter to Elif Köksal-Ersöz elif.koksal@inria.fr and Vincent Magloire vincent.magloire@inserm.fr.

    Related Publications:

    1. Magloire V, Mercier MS, Kullmann DM, Pavlov I. GABAergic Interneurons in Seizures: Investigating Causality With Optogenetics. Neuroscientist. 2019 Aug 1;25(4):344–58.
    2. Köksal-Ersöz E, Modolo J, Bartolomei F, Wendling F. Neural mass modeling of slow-fast dynamics of seizure initiation and abortion. PLoS Comput. Biol., 16(11): e1008430, 2020.
    3. Köksal-Ersöz E, Yochum M, Benquet P, Wendling F. eCOALIA: Neocorticalneural mass model for simulating electroencephalographic signal, SoftwareX. 2024, 28: 101924.

    Master Internship Offer 2

  • Deciphering Nested Neural Rhythms During Visual Attention Tasks

    Neural activity in the brain spans multiple scales, encompassing both spatial (inter- and intra-areal) and temporal (slow and fast) dynamics. It is hypothesized that slower rhythms (alpha/beta bands) mediate top-down control, while faster rhythms (gamma band) facilitate bottom-up processing. During cognitive tasks, these rhythms interact and nest within mesoscale and macroscale circuits. This internship will focus on investigating the nested neural rhythms recorded during a visual-attention task. The objective is to uncover the relationship between the neural substrates and temporal properties of these nested rhythms, leveraging mathematical models that incorporate the laminar circuitry of neocortical columns. Familiarity with dynamical systems, mathematical models and proficiency with Python are required. The intern will join the Cophy Team hosted at the Center for Neuroscience Research of Lyon (CRNL), France. The ideal start date is January 2026, with some flexibility.

    Candidates should send their CV and a motivation letter to Elif Köksal-Ersöz elif.koksal@inria.fr and Mathilde Bonnefond mathilde.bonnefond@inserm.fr .

    Related Publications:

    1. Bonnefond M, Kastner S, Jensen O. Communication between Brain Areas Based on Nested Oscillations. eNeuro. 2017 Mar 1;4(2).
    2. Köksal-Ersöz E, Lazazzera R, Yochum M, Merlet I, Makhalova J, Mercadal B, et al. Signal processing and computational modeling for interpretation of SEEG-recorded interictal epileptiform discharges in epileptogenic and non-epileptogenic zones. J Neural Eng. 2022 Sep;19(5):055005.
    3. Sanchez-Todo R, Bastos AM, Lopez-Sola E, Mercadal B, Santarnecchi E, Miller EK, et al. A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings. NeuroImage. 2023 Apr 15;270:119938.
    4. Zheng T, Sugino M, Jimbo Y, Ermentrout GB, Kotani K. Analyzing top-down visual attention in the context of gamma oscillations: a layer- dependent network-of- networks approach. Front Comput Neurosci. 2024 Sep 23;18.

    Master Internship Offer 3

  • Chunking dynamics in sequential learning

    Chunking is a remarkable cognitive process that allows the brain to organize information into larger, meaningful units, enabling efficient memory and processing. For example, instead of remembering a sequence like "1234567890," we naturally group it as "(123) 456-7890." In the context of motor learning, chunking serves as a key mechanism for integrating motor sequences, streamlining complex actions into cohesive, automated patterns. This process evolves dynamically during learning: chunks become progressively fewer and longer, reflecting reorganization strategies that optimize performance. This internship will explore the evolution of chunking patterns during visuomotor sequence learning in baboons using a mathematical model of associative memory. The goal is to uncover the dynamical principles of chunk reorganization during learning. Familiarity with dynamical systems, mathematical models and proficiency with Python are required.

    The intern will join the Cophy Team hosted at the Center for Neuroscience Research of Lyon (CRNL), France. The ideal start date is January 2026, with some flexibility.

    Candidates should send their CV and a motivation letter to Elif Köksal-Ersöz elif.koksal@inria.fr

    Related Publications:

    1. Fonollosa J, Neftci E, Rabinovich M. Learning of Chunking Sequences in Cognition and Behavior. Sporns O, editor. PLOS Computational Biology. 2015;11(11):e1004592.
    2. Köksal-Ersöz E, Aguilar C, Chossat P, Krupa M, Lavigne F. Neuronal mechanisms for sequential activation of memory items: Dynamics and reliability. PLOS ONE. 2020;15(4):e0231165.
    3. Tosatto L, Fagot J, Nemeth D, Rey A. The Evolution of Chunks in Sequence Learning. Cognitive Science. 2022;46(4):e13124.