2025‌Activity reportProject-TeamMATHNEURO‌​‌

3.1.1​‌ Neuronal networks dynamics

The​​ study of neuronal networks​​​‌ is certainly motivated by​ the long term goal​‌ to understand how brain​​ is working. But, beyond​​​‌ the comprehension of brain​ or even of simpler​‌ neural systems in less​​ evolved animals, there is​​​‌ also the desire to​ exhibit general mechanisms or​‌ principles at work in​​ the nervous system. One​​​‌ possible strategy is to​ propose mathematical models of​‌ neural activity, at different​​ space and time scales,​​​‌ depending on the type​ of phenomena under consideration.​‌ However, beyond the mere​​ proposal of new models,​​​‌ which can rapidly result​ in a plethora, there​‌ is also a need​​ to understand some fundamental​​​‌ keys ruling the behavior​ of neuronal networks, and,​‌ from this, to extract​​ new ideas that can​​​‌ be tested in real​ experiments. Therefore, there is​‌ a need to make​​ a thorough analysis of​​​‌ these models. An efficient​ approach, developed in our​‌ team, consists of analyzing​​ neuronal networks as dynamical​​​‌ systems. This allows to​ address several issues. A​‌ first, natural issue is​​ to ask about the​​​‌ (generic) dynamics exhibited by​ the system when control​‌ parameters vary. This naturally​​ leads to analyze the​​​‌ bifurcations 2 occurring in​ the network and which​‌ phenomenological parameters control these​​ bifurcations. Another issue concerns​​​‌ the interplay between the​ neuron dynamics and the​‌ synaptic network structure.

3.1.2​​ Mean-field and stochastic approaches​​​‌

Modeling neural activity at​ scales integrating the effect​‌ of thousands of neurons​​ is of central importance​​​‌ for several reasons. First,​ most imaging techniques are​‌ not able to measure​​ individual neuron activity (microscopic​​​‌ scale), but are instead​ measuring mesoscopic effects resulting​‌ from the activity of​​ several hundreds to several​​​‌ hundreds of thousands of​ neurons. Second, anatomical data​‌ recorded in the cortex​​ reveal the existence of​​​‌ structures, such as the​ cortical columns, with a​‌ diameter of about 50​​ $\mu m$ to 1​​​‌ $mm$, containing​ of the order of​‌ ${10}^2$ to ${\mathrm{10}}^5$ neurons belonging to​​ a few different species.​​​‌ The description of this‌ collective dynamics requires models‌​‌ which are different from​​ individual neurons models. In​​​‌ particular, when the number‌ of neurons is large‌​‌ enough, averaging effects appear,​​ and the collective dynamics​​​‌ is well described by‌ an effective mean-field, summarizing‌​‌ the effect of the​​ interactions of a neuron​​​‌ with the other neurons,‌ and depending on a‌​‌ few effective control parameters.​​ This vision, inherited from​​​‌ statistical physics requires that‌ the space scale be‌​‌ large enough to include​​ a large number of​​​‌ microscopic components (here neurons)‌ and small enough so‌​‌ that the region considered​​ is homogeneous.

Our group​​​‌ is both using and‌ developing mathematical methods allowing‌​‌ to study neural activity​​ at multiple temporal 19​​​‌ and spatial scales 4‌, reproducing and predicting‌​‌ brain states in both​​ healthy 14 and pathological​​​‌ conditions 25, 26‌.

3.1.3 Neural fields‌​‌

Neural fields are a​​ phenomenological way of describing​​​‌ the activity of a‌ population of neurons by‌​‌ integro-differential equations. This continuous​​ approximation turns out to​​​‌ be very useful to‌ model large brain areas‌​‌ such as those involved​​ in migraine and visual​​​‌ perception. The mathematical properties‌ of these equations and‌​‌ their solutions are still​​ imperfectly known, in particular​​​‌ in the presence of‌ delays, different time scales‌​‌ and noise.

Our group​​ is developing mathematical and​​​‌ numerical methods for analyzing‌ these equations. These methods‌​‌ are based upon techniques​​ from functional analysis, bifurcation​​​‌ theory, equivariant bifurcation analysis,‌ delay equations, and stochastic‌​‌ partial differential equations. We​​ have been able to​​​‌ characterize the solutions of‌ these neural fields equations‌​‌ and their bifurcations, apply​​ and expand the theory​​​‌ to account for such‌ perceptual phenomena as edge,‌​‌ texture 44, and​​ motion perception. We have​​​‌ developed a neural field‌ model to study migraine-related‌​‌ phenomena 30. We​​ have also developed a​​​‌ theory of singular perturbations‌ for neural fields equations‌​‌ 3, based in​​ particular on center manifold​​​‌ and normal forms ideas‌ 4.

3.1.4 Slow-fast‌​‌ dynamics in neuronal models​​

Computation of the excitability​​​‌ threshold in simple multiple-timescale‌ models, where it appears‌​‌ as a slow manifold.​​ Top panels: in the​​​‌ FitzHugh-Nagumo spiking model; bottom‌ panels: in the Hindmarsh-Rose‌​‌ bursting model.

Neuronal rhythms typically​​​‌ display many different timescales,‌ therefore it is important‌​‌ to incorporate this slow-fast​​ aspect in models. We​​​‌ are interested in this‌ modeling paradigm where slow-fast‌​‌ point models, using Ordinary​​ Differential Equations (ODEs), are​​​‌ investigated in terms of‌ their bifurcation structure and‌​‌ the patterns of oscillatory​​ solutions that they can​​​‌ produce. To gain insight‌ into the dynamics of‌​‌ such systems, we use​​ a mix of theoretical​​​‌ techniques — such as‌ geometric desingularization and centre‌​‌ manifold reduction 57 —​​ and numerical methods such​​​‌ as pseudo-arclength continuation 49‌. We are interested‌​‌ in families of complex​​​‌ oscillations generated by both​ mathematical and biophysical models​‌ of neurons. In particular,​​ so-called mixed-mode oscillations (MMOs)​​​‌ 16, 47,​ 56, which represent​‌ an alternation between subthreshold​​ and spiking behavior, and​​​‌ bursting oscillations 48,​ 55, also corresponding​‌ to experimentally observed behavior​​ 45 (see Figure 1​​​‌). We are working​ on extending these results​‌ to spatio-temporal neural models​​ 3.

3.3.1 Modeling associative​​​‌ memory

The processes by​ which memories are formed​‌ and stored in the​​ brain are multiple and​​​‌ not yet fully understood.​ What is hypothesized so​‌ far is that memory​​ formation is related to​​​‌ the activation of certain​ groups of neurons in​‌ the brain. Then, one​​ important mechanism to store​​​‌ various memories is to​ associate certain groups of​‌ memory items with one​​ another, which then corresponds​​​‌ to the joint activation​ of certain neurons within​‌ different subgroup of a​​ given population. In this​​​‌ framework, plasticity is key​ to encode the storage​‌ of chains of memory​​ items. Yet, there is​​​‌ no general mathematical framework​ to model the mechanism(s)​‌ behind these associative memory​​ processes. We are aiming​​​‌ at developing such a​ framework using our expertise​‌ in multi-scale modeling, by​​ combining the concepts of​​​‌ heteroclinic dynamics, slow-fast dynamics​ and stochastic dynamics.

The​‌ general objective that we​​ wish to pursue in​​​‌ this project is to​ investigate non-equilibrium phenomena pertinent​‌ to storage and retrieval​​ of sequences of learned​​​‌ items. In previous works​ by team members 13​‌, 1, 22​​, it was shown​​​‌ that with a suitable​ formulation, heteroclinic dynamics combined​‌ with slow-fast analysis in​​ neural field systems can​​​‌ play an organizing role​ in such processes, making​‌ the model accessible to​​ a thorough mathematical analysis.​​​‌ Multiple choice in cognitive​ processes require a certain​‌ flexibility in the neural​​ network, which has recently​​​‌ been investigated in the​ article 23.

Our​‌ goal is to contribute​​ to identify general processes​​​‌ under which cognitive functions​ can be organized in​‌ the brain.

3.3.2 Decision-making​​

Decision-making refers to making​​ a choice between multiple​​​‌ alternatives. It is important‌ to make the choice‌​‌ by taking into account​​ short- and long-term consequences​​​‌ of each alternative. This‌ requires complex interactions between‌​‌ intricate neural mechanisms. These​​ mechanisms are far from​​​‌ being fully understood.

Our‌ goal is to contribute‌​‌ to a better understanding​​ of the neural mechanisms​​​‌ relevant to decision-making. For‌ this, we develop computational‌​‌ models based on mean-field​​ approximations of large dimensional​​​‌ neuronal networks. We test‌ our models on experimental‌​‌ data at behavioral level​​ 5. Finally, these​​​‌ mean-field models can be‌ integrated to brain simulators.‌​‌ This is useful to​​ study decision-making processes at​​​‌ the whole-brain scale.

3.3.3‌ Visual perception

Visual perception‌​‌ in human, non-human primates​​ and in many other​​​‌ mammalian species is achieved‌ throughout several cortical processes.‌​‌ The primary visual cortex​​ (V1) is the part​​​‌ of the brain which‌ is responsible for the‌​‌ first step in the​​ processing of visual input​​​‌ 53, 67.‌ This processing allows to‌​‌ identify local features of​​ the objects in a​​​‌ visual scene and integrating‌ these features to provide‌​‌ a global representation of​​ the objects in V1.​​​‌ This is crucial for‌ a complete visual perception‌​‌ of the objects.

Our​​ goal is to contribute​​​‌ to the understanding of‌ V1 functional architecture 61‌​‌, 62. To​​ achieve it, we use​​​‌ a neurogeometric approach. We‌ develop geometric models of‌​‌ V1 by using tools​​ from differential geometry and​​​‌ partial differential equations 6‌. We apply our‌​‌ geometric models to visual​​ perception phenomena and pathological​​​‌ dynamics generating visual hallucinations‌ 41. This will‌​‌ effectively embed the topic​​ of neurogeometry within the​​​‌ thematics of (hyper)excitability 37‌.

6.1.1​​​‌ Data sciences for MathNeuro​

Participants: Fabien Campillo,​‌ Mathieu Desroches, Serafim​​ Rodrigues [BCAM, Spain].​​​‌

MathNeuro aims to develop​ a more systematic Data​‌ Science approach to data​​ acquisition and analysis. In​​​‌ this context, we developed​ two tools this year.​‌

First, a Jupyter Book​​ entitled “Data Science and​​​‌ Spikes”, designed as​ a practical and accessible​‌ entry point into data​​ science through the exploration​​​‌ of electrophysiological data, in​ particular spike train recordings​‌ [GitHub, online​​ book, PDF].​​​‌

Second, the tool called​ PEPYNA for “Python-based Electrophysiology​‌ Neuron Yield & Analysis”'​​ [GitHub] focuses​​​‌ on the analysis of​ electrophysiological data, initially targeting​‌ Axon Binary Format (ABF)​​ files and with the​​​‌ potential to extend to​ other data formats. It​‌ involves the processing, visualization,​​ and basic statistical analysis​​​‌ of single-neuron datasets using​ Python scripts and notebooks.​‌ The long-term objective of​​ this tool is to​​​‌ evolve into a reusable​ and extensible software library.​‌

6.1.2 EBRAINS-EITN Fall School​​ in Computational Neuroscience 2025​​​‌

Participant: Emre Baspinar.​

Emre Baspinar prepared the​‌ Jupyter notebooks “TVB-AdEx-Tutorial-EBRAINS-EITN-Fall-School​​" for the hands-on​​​‌ session entitled “The Virtual​ Brain environment for whole-brain​‌ models", which he organized​​ in EBRAINS-EITN Fall School​​​‌ in Computational Neuroscience 2025​, Marseille, on November​‌ 24, 2025.

7.1.1 Neuronal networks​​ and neural fields

Participants:​​​‌ Collaboration: Emre Baspinar,​ Fabien Campillo, Mathieu​‌ Desroches, Daniele Avitabile​​ [VU Amsterdam], Damien​​​‌ Depannemaecker [INS, AMU],​ Massimo Mantegazza [IPMC, Inserm,​‌ Sophia Antipolis].

This​​ research theme is developed​​​‌ in close collaboration with​ Daniele Avitabile, Professor​‌ at VU Amsterdam, a​​ renowned specialist in mathematical​​​‌ models in neuroscience, as​ well as in numerical​‌ analysis and numerical implementation.​​

Multiple-timescale dynamics of neuronal​​​‌ networks

We have initiated​ about one year ago​‌ a new collaboration with​​ the Institut de Neuroscience​​​‌ des Systèmes in Marseille,​ in particular with Damien​‌ Depannemaecker and Viktor Jirsa,​​ on multiple-timescale dynamics of​​​‌ neuronal networks. We have​ already one article accepted​‌ for publication, described next.​​

To model the dynamics​​​‌ of neuron membrane excitability,​ many models can be​‌ considered, from the most​​ biophysically detailed to the​​ highest level of phenomenological​​​‌ description. Recent works at‌ the single neuron level‌​‌ have shown the importance​​ of taking into account​​​‌ the evolution of slow‌ variables such as ionic‌​‌ concentration. A reduction of​​ such a model to​​​‌ models of the integrate-and-fire‌ family is interesting to‌​‌ then go to large​​ network models. In this​​​‌ paper, we introduce a‌ way to consider the‌​‌ impairment of ionic regulation​​ by adding a third,​​​‌ slow, variable to the‌ adaptive Exponential integrate-and-fire model‌​‌ (AdEx). We then implement​​ and simulate a network​​​‌ including this model. We‌ find that this network‌​‌ was able to generate​​ normal and epileptic discharges.​​​‌ This model should be‌ useful for the design‌​‌ of network simulations of​​ normal and pathological states.​​​‌ The manuscript 31 has‌ been accepted for publication‌​‌ in Journal of Computational​​ Neuroscience.

Cortical spreading​​​‌ depolarization (CSD)

We are‌ also pursuing our long-term‌​‌ project on the modeling​​ of hyperexcitability, in particular​​​‌ in the context of‌ Cortical spreading depolarization (CSD).‌​‌ This collaboration is historical​​ in MathNeuro and it​​​‌ continues to be the‌ subject of a strong‌​‌ collaboration with Massimo Mantegazza​​ (IPMC, Inserm, Sophia Antipolis).​​​‌ Recently, we have extended‌ our temporal model for‌​‌ the initiation of CSD​​ (topic of the PhD​​​‌ thesis of Louisiane Lemaire‌ in MathNeuro, defended in‌​‌ 2021) towards spatially-extended activity​​ to account for the​​​‌ propagation of the phenomenon‌ throughout the cortex. Emre‌​‌ Baspinar is now actively​​ participating to the project,​​​‌ which constituted a part‌ of his research program‌​‌ for his recruitment at​​ Inria. The latest work​​​‌ 30, recently published‌ in the journal PLoS‌​‌ Computational Biology, is​​ described below.

CSD is​​​‌ a wave of neural‌ depolarization that initiates locally‌​‌ and then slowly spreads​​ across the cortex. It​​​‌ is characterized by (i)‌ initial neural hyperexcitability, (ii)‌​‌ prolonged neural silence following​​ the hyperexcitability. CSD is​​​‌ implicated in several pathologies,‌ particularly in migraine. Although‌​‌ there is an extensive​​ literature on the role​​​‌ of excitatory neurons in‌ CSD, much less is‌​‌ known about the role​​ of inhibitory neurons.

In​​​‌ this work, we study‌ the role of inhibitory‌​‌ neurons in migraine-related CSD​​ initiation and propagation, at​​​‌ both experimental and computational‌ levels. We perform experiments‌​‌ in mouse brain slices​​ and develop a novel​​​‌ computational model to unveil‌ the mechanisms underlying the‌​‌ experimental results. In the​​ experiments, we test the​​​‌ role of inhibitory neurons‌ in CSD propagation by‌​‌ modulating their activity with​​ optogenetic and pharmacological tools.​​​‌ In the modeling part,‌ we simulate the activity‌​‌ of large excitatory and​​ inhibitory populations of neurons​​​‌ by using a neural‌ field model. The model‌​‌ is based on an​​ excitatory-inhibitory population pair which​​​‌ is coupled to a‌ potassium concentration variable. Our‌​‌ experimental and simulation results​​ show that the decrease​​​‌ of the synaptic activity‌ of inhibitory neurons can‌​‌ enhance CSD propagation, because​​ of the reduction of​​​‌ the inhibitory synaptic weight,‌ whereas their spiking activity‌​‌ can enhance CSD propagation​​ because of increased extracellular​​​‌ potassium.

We are currently‌ exploring the role of‌​‌ astrocytes in the propagation​​​‌ of CSD, which was​ the topic of the​‌ internship of Camilla Nouveau​​ in MathNeuro, which started​​​‌ in September 2024 and​ ended in February 2025.​‌ A manuscript is in​​ preparation, it should be​​​‌ submitted in the winter​ 2026. This work induced​‌ a collaboration on the​​ role of astrocytes in​​​‌ CSD, with Etienne Audinat,​ DR CNRS from Institute​‌ of Functional Genomics (IGF)​​, Montpellier. We applied​​​‌ for an ANR JCJC​ 2026 research grant in​‌ the context of this​​ collaboration.

Mean-field limits of​​​‌ inhibitory neuronal networks

We​ have also initiated a​‌ collaboration with Boris Gutkin​​ and Alex Cayco-Gajic from​​​‌ the Group for Neural​ Theory at the École​‌ Normale Supérieure of Paris​​ on mean-field limits of​​​‌ inhibitory neuronal networks connected​ by gap junction, in​‌ link with the electrical​​ activity of the cerebellum.​​​‌ This collaboration took shape​ within the PhD project​‌ of Hélène Todd (​​Group for Neural Theory​​​‌) and it gave​ rise to one joint​‌ article published this year​​ in the journal Physical​​​‌ Review E, available​ as 36.

In​‌ this work, we study​​ networks of inhibitory interneurons,​​​‌ which are essential for​ regulating the activity of​‌ principal neurons, especially by​​ inducing temporally patterned dynamic​​​‌ states. We aim to​ understand the dynamic mechanisms​‌ that allow for synchronization​​ to arise in networks​​​‌ of electrically and chemically​ coupled interneurons. To this​‌ end, we use the​​ `exact' mean-field reduction, using​​​‌ the Ott-Antonsen ansatz already​ studied in the MathNeuro​‌ team (see 2 and​​ 28) to derive​​​‌ a neural mass model​ for both homogeneous and​‌ clustered networks. We first​​ analyze a single population​​​‌ of neurons to understand​ how the two couplings​‌ interact with one another.​​ We demonstrate that the​​​‌ network transitions from an​ asynchronous to a synchronous​‌ regime either by increasing​​ the strength of the​​​‌ gap junction connectivity or​ the strength of the​‌ background input current. Conversely,​​ the strength of inhibitory​​​‌ synapses affects the population​ firing rate, suggesting that​‌ electrical and chemical coupling​​ strengths act as complementary​​​‌ mechanisms by which networks​ can tune synchronous oscillatory​‌ behavior. Next, inspired by​​ the existence of multiple​​​‌ interconnected interneuron subtypes in​ the cerebellum, we analyze​‌ networks consisting of two​​ clusters of cell types​​​‌ defined by differing chemical​ versus electrical coupling strengths.​‌ We show that breaking​​ the electrical and chemical​​​‌ coupling symmetry between these​ clusters induces bistability, so​‌ that a transient external​​ input can switch the​​​‌ network between synchronous and​ asynchronous firing. Together, our​‌ results show the variety​​ of cell-intrinsic and network​​​‌ properties that contribute to​ synchronization of interneuronal networks​‌ with multiple types of​​ coupling.

7.1.2 Multiple-timescale dynamics​​​‌ at the level of​ single neurons

Participants: Collaboration:​‌ Fabien Campillo, Mathieu​​ Desroches, Serafim Rodrigues​​​‌ [BCAM, Spain], Piotr​ Kowalczyk [University of Wrocław,​‌ Poland], Joaquin Piriz​​ [Achucarro Basque Center for​​​‌ Neuroscience, Spain].

Integrate-and-fire​ model of single neuron​‌ activity

We have an​​ ongoing collaboration with Piotr​​​‌ Kowalczyk (Wrocław University, Poland)​ on integrate-and-fire model of​‌ single neuron activity. Over​​ the past year, we​​ have finalized and published​​​‌ a second article on‌ the topic, following 18‌​‌. In this new​​ work, we present a​​​‌ computational study of the‌ Conductance-Based Adaptive Exponential (CAdEx)‌​‌ integrate-and-fire neuronal model, focusing​​ on its multiple timescale​​​‌ nature, and on how‌ it shapes its main‌​‌ dynamical regimes. In particular,​​ we show that the​​​‌ spiking and so-called delayed‌ bursting regimes of the‌​‌ model are triggered by​​ discontinuity-induced bifurcations that are​​​‌ directly related to the‌ multiple-timescale aspect of the‌​‌ model, and are mediated​​ by canard solutions. By​​​‌ means of a numerical‌ bifurcation analysis of the‌​‌ model, using the software​​ package Coco 46,​​​‌ we can precisely describe‌ the mechanisms behind these‌​‌ dynamical scenarios. Spike-increment transitions​​ are revealed. These transitions​​​‌ are accompanied by a‌ fold and a period-doubling‌​‌ bifurcation, and are organized​​ in parameter space along​​​‌ an isola of periodic‌ solutions with resets. Finally,‌​‌ we also unveil the​​ presence of a homoclinic​​​‌ bifurcation terminating a canard‌ explosion which, together with‌​‌ the presence of resets,​​ organizes the delayed bursting​​​‌ regime of the model.‌ This work has been‌​‌ partially done during stays​​ of Serafim Rodrigues in​​​‌ MathNeuro, and the manuscript‌ 32 has been published‌​‌ in the Bulletin of​​ Mathematical Biology.

Multiscale​​​‌ mathematical model of recordings‌ of LHb neural data‌​‌

This year, we have​​ pursued our new collaboration​​​‌ with an experimental group‌ in Bilbao, at Achucarro‌​‌ the Basque Center for​​ Neuroscience, together with our​​​‌ close collaborator Serafim Rodrigues‌ (BCAM). Namely, we are‌​‌ now working with the​​ lab of Joaquin Piriz,​​​‌ a specialist of mood‌ disorder, in particular in‌​‌ link with a specific​​ brain region, the lateral​​​‌ Habenula (LHb). Mathieu Desroches‌ and Fabien Campillo, together‌​‌ with Serafim Rodrigues, are​​ working on a multiscale​​​‌ mathematical model of recordings‌ of LHb neural data‌​‌ collected by Joaquin Piriz.​​ LHb neurons have general​​​‌ bursting activity and there‌ are already a number‌​‌ of studies modeling these.​​ The novelty here is​​​‌ the presence of two‌ different bursting patterns observed‌​‌ in the data from​​ the Piriz lab. We​​​‌ have made a phenomenological‌ multiple-timescale model generating these‌​‌ two bursting patterns based​​ upon the behavior of​​​‌ two slow processes, which‌ we suspect are related‌​‌ to slow potassium and​​ calcium currents. The model​​​‌ reproduces the collected data,‌ both in a deterministic‌​‌ and in a stochastic​​ environment. We are also​​​‌ developing a burst-detection algorithm‌ in order to be‌​‌ able to automatically analyze​​ the data from our​​​‌ collaborator. A manuscript has‌ been submitted for publication‌​‌ and it is currently​​ in revision in The​​​‌ Journal of Physiology and‌ it is available as‌​‌ 40.

We are​​ currently working on building​​​‌ up a biophysical model‌ reproducing these bursting patterns‌​‌ and that will allow​​ us to zoom into​​​‌ the biological mechanisms underpinning‌ this coexistence of activity‌​‌ patterns in LHb neurons.​​ This work is at​​​‌ the core of the‌ Master internship project of‌​‌ Natalie Elena Cernei (University​​ of Bologna), which started​​​‌ in October 2025 and‌ will end in February‌​‌ 2026.

7.1.3 Multiscale modeling​​​‌ of Dravet Syndrome

Participants:​ Fabien Campillo, Mathieu​‌ Desroches, Louisiane Lemaire​​, Serafim Rodrigues [BCAM,​​​‌ Spain].

This project​ started in early 2024,​‌ and is funded by​​ the Exploratory action 2MDS​​​‌. In particular, we​ recruited Louisiane Lemaire as​‌ postdoc to work on​​ this project. She is​​​‌ an expert in the​ modeling of hyperexcitability, in​‌ particular in the context​​ of ion channel mutations.​​​‌ She had done her​ PhD in MathNeuro on​‌ the CSD project. She​​ joined our team in​​​‌ Montpellier in April 2025.​

Dravet syndrome is a​‌ developmental and epileptic encephalopathy​​ (DEE) that typically begins​​​‌ in the first year​ of life. This complex​‌ pathology is characterized by​​ drug-resistant seizures, various comorbidities​​​‌ such as cognitive delay,​ and a risk of​‌ early death. Most cases​​ are due to mutations​​​‌ of Na${}_{\mathrm{V}}$1.1,​ a voltage-gated sodium channel​‌ expressed in fast-spiking (FS)​​ inhibitory neurons. The pathological​​​‌ mechanism in the initial​ stage of the disease​‌ involves impaired function of​​ those neurons, leading to​​​‌ network hyperexcitability. However, the​ details remain unclear.

Mutations​‌ of Na${}_{\mathrm{V}}$1.1​​ may result in non-functional​​​‌ channels or channels with​ altered gating properties. We​‌ focus on the less​​ studied case of altered​​​‌ gating, by investigating how​ it impairs neuronal activity​‌ in the case of​​ a specific mutation (A1783V).​​​‌ Using recordings in cell​ lines, Layer et al.​‌ 58 showed that A1783V​​ alters the voltage dependence​​​‌ of channel activation, as​ well as the voltage​‌ dependence and kinetics of​​ slow inactivation. Slow inactivation​​​‌ is a mechanism distinct​ from the fast inactivation​‌ of sodium channels at​​ each spike, developing much​​​‌ more slowly, during prolonged​ trains of depolarization. Implementing​‌ the three effects of​​ the mutation in a​​​‌ conductance-based model, Layer et​ al. predict that altered​‌ activation has the largest​​ impact on channel function,​​​‌ as it causes the​ most severe reduction in​‌ firing rate.

Using conductance-based​​ models tailored to the​​​‌ dynamics of FS inhibitory​ neurons, we examine how​‌ the three alterations affect​​ susceptibility to depolarization block,​​​‌ another firing deficit aside​ from frequency reduction. We​‌ look deeper into slow​​ inactivation, exploiting the timescale​​​‌ difference with the rest​ of the system. We​‌ find that slow inactivation​​ of mutant channels at​​​‌ lower voltage values than​ wild type channels favors​‌ depolarization block upon sustained​​ stimulation. More precisely, shifting​​​‌ the steady-state voltage dependence​ of slow inactivation destroys​‌ the stable limit cycle​​ of the full system​​​‌ corresponding to tonic spiking,​ and creates a stable​‌ equilibrium corresponding to depolarization​​ block. The accelerated kinetics​​​‌ of slow inactivation in​ mutant channels hastens the​‌ transition from tonic spiking​​ to depolarization block. These​​​‌ findings suggest that alterations​ of Na${}_{\mathrm{V}}$1.1​‌ slow inactivation should not​​ be neglected as they​​​‌ might play an important​ pathological role, adding to​‌ the conclusions of Layer​​ et al. on the​​​‌ consequences of altered Na​${}_{\mathrm{V}}$1.1 activation.

This​‌ work was published in​​ 2025 in the journal​​​‌ Scientific Reports, and​ it is available as​‌ 35.

7.1.4 Neuronal​​ excitability: Interface with electrophysiological​​ experiments

Participants: Collaboration: Fabien​​​‌ Campillo, Mathieu Desroches‌, Serafim Rodrigues [BCAM,‌​‌ Spain], Jan Sieber​​ [University of Exeter, UK]​​​‌.

Since 2024, several‌ key developments have occurred‌​‌ in the experimental laboratory​​ project at BCAM, the​​​‌ so-called NeuroMATH lab.‌ A dual protocol was‌​‌ implemented to study neuronal​​ excitability dynamics. The “voltage-clamp”​​​‌ (VC) protocol with a‌ slow ramp on the‌​‌ target stabilized membrane potential​​ to reveal the excitability​​​‌ structure of neurons, while‌ the “current-clamp” (CC) protocol‌​‌ with slow ramp on​​ the target confirmed the​​​‌ separation between slow and‌ fast components of the‌​‌ neuronal model. As a​​ result, we are currently​​​‌ investigating the bifurcation structure‌ of real neurons directly‌​‌ from experimental data, which​​ is a great tool​​​‌ to validate models as‌ well as explore the‌​‌ parameter space of neurons.​​ These initial results were​​​‌ published in 2025 in‌ the journal PLOS Computational‌​‌ Biology and available as​​ 29.

We are​​​‌ currently using a dynamic-clamp‌ protocol to adjust the‌​‌ neuron's behavior, transforming an​​ integrator neuron into a​​​‌ resonator neuron; we are‌ effectively confirming experimental and‌​‌ theoretical predictions obtained in​​ the PhD project of​​​‌ Guillaume Girier (successfully defended‌ in September 2024) and‌​‌ published in 2023 21​​. These experiments were​​​‌ initiated during a one-month‌ stay in Bilbao by‌​‌ Fabien Campillo and Mathieu​​ Desroches in the winter​​​‌ 2024, and they are‌ still running in order‌​‌ to obtain enough trials​​ and material prior to​​​‌ writing a research article.‌

7.1.5 Corticogenesis and exposome‌​‌

Participants: Fabien Campillo,​​ Mathieu Desroches.

In​​​‌ 2025, we have started‌ a collaboration with the‌​‌ Institute for Neurosciences of​​ Montpellier (INM), namely​​​‌ with the team of‌ Karine Loulier (Inserm). The‌​‌ collaboration is on the​​ effect of pollution on​​​‌ cortico-genesis. Loulier has obtained‌ funds from the ExposUM‌​‌ Institute of Montpellier, to​​ make progress on this​​​‌ topic. We have been‌ included to the project‌​‌ and will co-supervise with​​ her a Master internship​​​‌ in 2026 to start‌ deciphering her data.

7.3.1 Memory

Participants: Collaboration:‌​‌ Fabien Campillo, Pascal​​ Chossat, Mathieu Desroches​​​‌, Frédéric Lavigne [BCL,‌ Université Côte d'Azur, Nice]‌​‌.

We have continued​​ along the year 2025​​​‌ our research line on‌ modeling memory processes with‌​‌ different strategies, both with​​ dynamical systems and Bayesian​​​‌ inference.

Associative memory

In‌ the context of the‌​‌ ANR project HEBBIAN (PI:​​ Arnaud Rey, CNRS, Marseille),​​​‌ we are working on‌ modeling associative memory using‌​‌ multiple-timescale dynamical systems and​​ heteroclinic dynamics in the​​​‌ context of attractors networks.‌ This research line has‌​‌ given a number of​​ articles 1, 13​​​‌, 22, 24‌, all of them‌​‌ mostly focused on abstract​​ mechanism and computations. In​​​‌ the context of this‌ ANR project, we are‌​‌ working on data collected​​ in the lab of​​​‌ Arnaud Rey, and we‌ are adapting the model‌​‌ and its plasticity rules​​ to be able to​​​‌ reproduce the data.

In‌ 2025, the manuscript 34‌​‌ by Pascal Chossat, Elif​​ Köksal Ersöz and Frédéric​​​‌ Lavigne (BCL lab, Univ.‌ Côte d'Azur) was published‌​‌ in the journal PLOS​​ one. This work​​​‌ focuses on gain modulation‌ of actions selection without‌​‌ synaptic relearning. Adaptation of​​ behavior requires the brain​​​‌ to change goals in‌ a changing environment. Synaptic‌​‌ learning has demonstrated its​​ effectiveness in changing the​​​‌ probability of selecting actions‌ based on their outcome.‌​‌ In the extreme case,​​ it is vital not​​​‌ to repeat an action‌ to a given goal‌​‌ that led to harmful​​ punishment. The present model​​​‌ proposes a simple neural‌ mechanism of gain modulation‌​‌ that makes possible immediate​​ changes in the probability​​​‌ of selecting a goal‌ after punishment of variable‌​‌ intensity. Results show how​​ gain modulation determine the​​​‌ type of elementary navigation‌ process within the state‌​‌ space of a network​​ of neuronal populations of​​​‌ excitatory neurons regulated by‌ inhibition. Immediately after punishment,‌​‌ the system can avoid​​ the punished populations by​​​‌ going back or by‌ jumping to unpunished populations.‌​‌ This does not require​​ particular credit assignment at​​​‌ the “choice" population but‌ only gain modulation of‌​‌ neurons active at the​​ time of punishment. Gain​​​‌ modulation does not require‌ statistical relearning that may‌​‌ lead to further errors,​​ but can encode memories​​​‌ of past experiences without‌ modification of synaptic efficacies.‌​‌ Therefore, gain modulation can​​ complement synaptic plasticity.

Bayesian​​​‌ brain and the free‌ energy principle

The Principle‌​‌ of Free Energy (FEP)​​ is a mathematical and​​​‌ conceptual framework proposed by‌ Karl Friston 52.‌​‌ This principle posits that​​​‌ living organisms (their organs,​ brains, and the neurons​‌ therein) aim to minimize​​ surprise or uncertainty about​​​‌ their environment. According to​ this theory, this minimization​‌ is achieved by creating​​ internal models of their​​​‌ environment to predict sensory​ inputs. By reducing the​‌ discrepancy between predicted and​​ actual sensory data—referred to​​​‌ as free energy or​ surprise—organisms adapt effectively, learn,​‌ and make predictions. This​​ principle extends beyond the​​​‌ Bayesian brain hypothesis; it​ underpins theories of perception,​‌ action, and cognition while​​ offering insights into how​​​‌ biological systems maintain stability​ and navigate complex environments​‌ through predictive processing. Although​​ proposed nearly two decades​​​‌ ago, this theory has​ gained significant traction in​‌ recent years, spreading far​​ beyond neuroscience and into​​​‌ other scientific fields. Furthermore,​ experimental validations have begun​‌ to confirm its premises.​​ While this approach generates​​​‌ excitement, it also faces​ criticism for its abstract​‌ and ambitious nature.

This​​ year, we have started​​​‌ to address this topic​ through the lens of​‌ nonlinear filtering (NLF) 12​​. Bayesian NLF, an​​​‌ area of expertise for​ Fabien Campillo (who leads​‌ this research line in​​ Mathneuro), is a fundamental​​​‌ yet underexplored component of​ the FEP. This novel​‌ research direction within MathNeuro​​ has benefited from a​​​‌ new collaboration with Jérémie​ Naudé, a CNRS senior​‌ researcher at the Institute​​ of Functional Genomics (IGF)​​​‌ in Montpellier. This collaborative​ effort aims to provide​‌ a deeper understanding of​​ the Free Energy Principle​​​‌ (FEP) and its applications,​ particularly in neuroscience.

7.3.2​‌ Decision-making

Participant: Collaboration: Emre​​ Baspinar, Alain Destexhe​​​‌ [NeuroPsi, CNRS, Saclay].​

This is a new​‌ research line in MathNeuro,​​ led by Emre Baspinar​​​‌ and for which he​ has brought to the​‌ team novel collaborations, in​​ particular with Alain Destexhe​​​‌ (CNRS, Paris-Saclay) and Rubén​ Moreno-Bote (UPF, Barcelona, Spain).​‌ This year, one project​​ is finalized and accepted​​​‌ for publication. It is​ described below.

Decision-making refers​‌ to choosing one of​​ the existing alternatives. A​​​‌ decision is often made​ based on a strategy​‌ which maximizes long term​​ benefits of the chosen​​​‌ alternatives. Neural mechanisms underlying​ strategy-based decision-making are far​‌ from fully understood. In​​ this work, we propose​​​‌ a strategy-based decision-making model​ to contribute to better​‌ understanding of relevant neural​​ mechanisms in human and​​​‌ macaque. The model is​ based on two neural​‌ populations. Each population is​​ composed of a pair​​​‌ of excitatory-inhibitory subpopulations in​ cortical layer 2/3. The​‌ model is biophysically plausible​​ since it is based​​​‌ on long-range cortico-cortical connections​ between the layer 2/3​‌ populations. These connections are​​ excitatory. This long-range excitation​​​‌ is conflicted by an​ inhibition based on local​‌ connections within the populations.​​ This configuration introduces a​​​‌ competition between the layer​ 2/3 populations, sufficient for​‌ making a decision to​​ choose between two alternatives​​​‌ shown on the monitor.​ We integrate the model​‌ with a learning mechanism.​​ This allows the model​​​‌ to learn the optimal​ decision-making strategy which maximizes​‌ the long term benefits.​​ We test the model​​​‌ on two decision-making tasks​ applied on human and​‌ macaque. This model elaborates​​ certain biophysical details, which​​ were not considered by​​​‌ the previous models proposed‌ for similar decision-making tasks.‌​‌ Finally, it can be​​ embedded in a brain​​​‌ simulator such as The‌ Virtual Brain to study‌​‌ large-scale brain dynamics. The​​ manuscript 5 has been​​​‌ accepted for publication in‌ journal PLoS One.‌​‌

7.3.3 Visual perception

Participant:​​ Emre Baspinar.

This​​​‌ is a new research‌ line in MathNeuro, led‌​‌ by Emre Baspinar and​​ for which he has​​​‌ brought to the team‌ novel collaborations, in particular‌​‌ with Giovanna Citti (University​​ of Bologna, Italy) and​​​‌ Alessandro Sarti (EHESS, Paris).‌ We participate to a‌​‌ Marie-Curie Horizon Europe Grant​​ 2026 application as collaborator,​​​‌ where Giovanna Citti and‌ Alessandro Sarti are the‌​‌ main PIs of the​​ project. Our collaboration in​​​‌ the project is based‌ on numerical methods for‌​‌ neurogeometric models of visual​​ perception.

Neural fields refer​​​‌ to integro-differential equations which‌ model the average neural‌​‌ activity of a neural​​ population at a coarse-grained​​​‌ limit. In classical neural‌ fields the neural interactions‌​‌ are modeled based on​​ a distance-based connectivity, without​​​‌ taking into account the‌ modulatory effects of functional‌​‌ properties of neurons on​​ the connectivity. Such effects​​​‌ are observed in particular‌ in the primary visual‌​‌ cortex (V1) and can​​ be modeled by using​​​‌ neurogeometric approach. In this‌ work, we consider a‌​‌ neural field which takes​​ into account these effects​​​‌ in the connectivity by‌ focusing on the functional‌​‌ architecture of V1. We​​ discuss the potential of​​​‌ this neural field to‌ an extension towards pathological‌​‌ cortical activity. This work​​ was presented in Geometric​​​‌ Science of Information 2025,‌ Saint-Malo and published as‌​‌ a conference paper 37​​.

8.1.1​​ ANR projects

8.1.2​‌ Inria Exploratory Action

8.2.1 Unmute​

• Title:
  Unraveling and Modeling​‌ the aggravation of ASD​​ symptoms following in Utero​​​‌ exposure to Environmental pollutant​ residues
• Duration:
  From September​‌ 1, 2025 to August​​ 31, 2027
• Inria contacts:​​​‌
  Fabien Campillo & Mathieu​ Desroches
• Coordinator:
  Karine Loulier​‌ (Institute of Neurosciences of​​ Montpellier, Inserm)
• Summary:
  Autism​​​‌ spectrum disorders (ASD) arise​ from a complex interplay​‌ of genetic and environmental​​ factors, and are characterized​​ by stereotyped behaviors, social​​​‌ interaction deficits and frequent‌ co-morbidities such as drug-resistant‌​‌ epilepsy. The multiplicity of​​ causative factors and phenotypic​​​‌ manifestations hinder the development‌ of targeted diagnostic or‌​‌ therapeutic tools that would​​ reduce or reverse the​​​‌ growing incidence of ASD‌ and improve patients' quality‌​‌ of life. Our preliminary​​ results obtained in a​​​‌ genetic mouse model of‌ ASD with drug-resistant epilepsies‌​‌ show aggravated social interaction​​ deficits and heterogeneous Focal​​​‌ Cortical Dysplasia (FCD)-type cortical‌ malformations in heterozygous newborn‌​‌ animals exposed prenatally to​​ a cocktail of three​​​‌ anilinopyrimidine fungicide residues. Our‌ project aims to link‌​‌ the structural features of​​ fungicide-induced FCD, such as​​​‌ the cell composition, occurrence‌ frequency, size, location in‌​‌ distinct cortical areas and​​ biochemical signature, with their​​​‌ effects on neuronal network‌ activity and behavioral deficits.‌​‌ We will investigate how​​ these malformations, exacerbated by​​​‌ environmental exposure in a‌ genetically susceptible background, contribute‌​‌ to the severity of​​ ASD and epilepsy. Our​​​‌ findings will help establish‌ predictive biomarkers to anticipate‌​‌ ASD severity and guide​​ therapeutic strategies. By bridging​​​‌ structural, functional, and behavioral‌ insights, this research will‌​‌ improve our understanding of​​ gene-environment relationship between ASD​​​‌ and fungicide exposure and‌ offer novel avenues for‌​‌ personalized intervention.

9.1.1 Scientific events: organization‌

9.1.2 Scientific events: selection‌

9.1.3 Journal

9.1.4 Invited talks‌

• Fabien Campillo and Mathieu‌​‌ Desroches gave an invited​​ seminar talk entitled “Présentation​​​‌ de l'équipe-projet Inria MathNeuro‌ : Mathematics for Neuroscience‌​‌” at the Institut​​ de Génomique Fonctionnelle”,​​​‌ Montpellier, April 18, 2025.‌
• Pascal Chossat gave an‌​‌ invited Keynote Lecture entitled​​ “Models for sequential association​​​‌ of learned concepts in‌ the cortex” at the‌​‌ Coupled 80 online conference,​​ October 9, 2025.
• Mathieu​​​‌ Desroches gave an invited‌ presentation entitled “Multiscale modeling‌​‌ of differential neurotransmitter release”​​ at the “Brain Plasticity​​​‌ & Modelisation” workshop, Montpellier,‌ January 24, 2025.
• Mathieu‌​‌ Desroches gave an invited​​​‌ presentation entitled “Observing hidden​ neuronal states in experiments”​‌ at the Applied Mathematics​​ Webinar, LAMSIN (Tunisie),​​​‌ February 5, 2025.
• Mathieu​ Desroches gave an invited​‌ online presentation entitled “Classifying​​ bursting oscillations using slow-fast​​​‌ dynamics” in the minisymposium​ `Patterns of Neural Activity'​‌ - Part 1 of​​ 2, SIAM Conference​​​‌ on Application of Dynamical​ Systems, Denver (USA),​‌ May 12, 2025.
• Mathieu​​ Desroches gave an invited​​​‌ online presentation entitled “Complex​ neuronal bursting oscillations: the​‌ role of slow variables”​​ at the Virtual SMB​​​‌ MathNeuro Mini-Conference, June​ 13, 2025.
• Mathieu Desroches​‌ gave an invited online​​ presentation entitled “Complex neuronal​​​‌ bursting oscillations: the role​ of slow variables” at​‌ the Secondes journées maths-bio​​ de la fédération OcciMath'​​​‌, Institut de Mathématiques​ de Toulouse, September 18,​‌ 2025.
• Mathieu Desroches gave​​ an invited online presentation​​​‌ entitled “Complex neuronal bursting​ oscillations: the role of​‌ slow variables” at the​​ Coupled 80 online conference,​​​‌ October 10, 2025.
• Emre​ Baspinar gave an invited​‌ minicourse entitled “Neural fields​​ as population models” at​​​‌ EBRAINS-EITN Fall School in​ Computational Neuroscience, Marseille,​‌ November 24, 2025.
• Emre​​ Baspinar gave an invited​​​‌ hands-on session entitled “The​ Virtual Brain environment for​‌ whole-brain models” at EBRAINS-EITN​​ Fall School in Computational​​​‌ Neuroscience, Marseille, November​ 24, 2025.
• Emre Baspinar​‌ gave an invited talk​​ entitled “Geometric neural fields​​​‌ for cortical activity” at​ Geometric Science of Information​‌, Saint Malo, October​​ 29, 2025.
• Emre Baspinar​​​‌ gave an invited talk​ entitled “A biologically plausible​‌ decision-making model based on​​ interacting neural populations” at​​​‌ NeuroMod meeting, Antibes,​ July 9, 2025.
• Emre​‌ Baspinar gave an invited​​ talk entitled “A computational​​​‌ model for cortical spreading​ depression” at SIAM Conference​‌ on Applied Dynamical Systems​​, Denver, USA, May​​​‌ 11, 2025.

9.1.5 Leadership​ within the scientific community​‌

• Fabien Campillo is a​​ founding member of the​​​‌ African scholarly Society on​ Digital Sciences (ASDS).

9.1.6​‌ Scientific expertise

• Emre Baspinar​​ has been reviewing grant​​​‌ proposals for Natural Sciences​ and Engineering Research Council​‌ of Canada.
• Mathieu​​ Desroches was reviewer of​​​‌ the PhD manuscript entitled​ “Emergence of complex dynamics​‌ in neuronal and glial​​ cells of the CNS”​​​‌ by Matteo Martin at​ the University of Bologna​‌ (Italy).

9.1.7 Research administration​​

• Emre Baspinar is mentoring​​​‌ the PhD seminar of​ the Inria Branch at​‌ the University of Montpellier.​​
• Fabien Campillo is member​​​‌ of the “Formation Spécialisée​ de Site” (FSS).
• Fabien​‌ Campillo is member of​​ the “Inria Evaluation Committee”​​​‌ (CE). In particular, he​ has been member of:​‌
  • the committee for the​​ advancement of the ISFP​​​‌ Researchers' Career,
  • the working​ group “Reflections on the​‌ Use of Generative AI​​ for Research Professions” (see​​​‌ 38),
  • the working​ group “Individual evaluation of​‌ researchers”,
  • the working groups​​ for the creation of​​​‌ 3 team-projects (COPHY, POPOPOP,​ NECTARINE),
  • the working groups​‌ for the evaluation of​​ the 3 team-projects (ERMINE,​​​‌ MIND, MNEMOSYNE).

9.2.1 Teaching

• Master:​​​‌ Emre Baspinar, Computational​ modeling of neural popoulations​‌ (Lectures, example classes and​​ computer labs), 24 hours​​ (Nov.-Dec. 2025), M1 (Mod4NeuCog),​​​‌ Université Côte d'Azur, Sophia‌ Antipolis, France.
• Master: Mathieu‌​‌ Desroches, Multiple Timescale​​ Dynamics in Neuroscience (Lectures,​​​‌ example classes and computer‌ labs), 9 hours (Jan.‌​‌ 2025), M1 (Mod4NeuCog), Université​​ Côte d'Azur, Sophia Antipolis,​​​‌ France.
• Master: Mathieu Desroches‌, Modèles Mathématiques et‌​‌ Computationnels en Neuroscience (Lectures,​​ example classes and computer​​​‌ labs), 8 hours (Feb.‌ 2025), M1 (BIM), Sorbonne‌​‌ Université, Paris, France.
• Master:​​ Mathieu Desroches, Modeling​​​‌ using Dynamical Systems (Lectures,‌ example classes and computer‌​‌ labs), 10 hours (Sep.-Oct.​​ 2025), M1 (IEAP), Université​​​‌ de Montpellier, Montpellier.
• Master:‌ Louisiane Lemaire, Modèles‌​‌ Mathématiques et Computationnels en​​ Neuroscience (Lectures, example classes​​​‌ and computer labs), 10‌ hours (Feb. 2025), M1‌​‌ (BIM), Sorbonne Université, Paris,​​ France.

9.2.2 Supervision

• Master​​​‌ 2 internship:
  Camilla Nouveau‌, University of Bologna‌​‌ (Italy), has done a​​ Master 2 internship on​​​‌ “Modeling of astro-neural population‌ dynamics and its application‌​‌ to cortical spreading depression",​​ supervised by Emre Baspinar​​​‌ , September 2024 -‌ February 2025.
• Master 2‌​‌ internship:
  Natalie Elena Cernei​​, University of Bologna​​​‌ (Italy), is doing a‌ Master 2 internship on‌​‌ “Multiscale modeling of Lateral​​ Habenula neurons", supervised by​​​‌ Mathieu Desroches , October‌ 2025 - February 2026.‌​‌

9.2.3 Juries

• Fabien Campillo​​ was a member of​​​‌ the jury for the‌ recruitment of a Junior‌​‌ Chair at the Inria​​ Branch at the University​​​‌ of Montpellier.
• Fabien Campillo‌ was a member of‌​‌ the jury for the​​ recruitment of directors of​​​‌ research (grade DR2) at‌ Inria.
• Fabien Campillo was‌​‌ a member of the​​ jury for the recruitment​​​‌ researchers (CRCN & ISFP)‌ at the Inria centre‌​‌ at the University Grenoble​​ Alpes.
• Fabien Campillo was​​​‌ a member of the‌ habilitation thesis jury (HDR,‌​‌ see this page)​​ of Coralie Fritsch at​​​‌ the Université de Lorraine,‌ December 15, 2025.
• Pascal‌​‌ Chossat was a member​​ of the Comité de​​​‌ Suivi Individuel (CSI) of‌ PhD student Martin Jalard‌​‌ at the Inria Center​​ at Univ. Côte d'Azur​​​‌ (Sophia Antipolis).
• Mathieu Desroches‌ was president of the‌​‌ jury (and reviewer of​​ the thesis manuscript) of​​​‌ the PhD defense of‌ Matthieu Aud'hui at the‌​‌ LTSI lab of the​​ University of Rennes, December​​​‌ 8, 2025.
• Mathieu Desroches‌ was jury member of‌​‌ the PhD defense of​​ Hélène Todd in the​​​‌ Group for Neural Theory‌, Ecole Normale Supérieure,‌​‌ Paris, June 30, 2025.​​
• Mathieu Desroches was a​​​‌ member of the Comité‌ de Suivi Individuel (CSI)‌​‌ of PhD student Sarah​​ Gaubi at Institut Pasteur​​​‌ (Inserm, Paris).
• Mathieu Desroches‌ was a member of‌​‌ the Comité de Suivi​​ Individuel (CSI) of PhD​​​‌ student Gabriele Casagrande at‌ Institut de Neurosciences des‌​‌ Systèmes (Aix-Marseille Université, Marseille).​​
• Emre Baspinar was a​​​‌ member of the Comité‌ de Suivi Individuel (CSI)‌​‌ of PhD student Jawad​​ Ali at Sorbonne Université,​​​‌ École doctorale de Sciences‌ Mathématiques de Paris Centre.‌​‌

International journals

• 29​​ articleD.Dmitry Amakhin​​​‌, A.Anton Chizhov​, G.Guillaume Girier​‌, M.Mathieu Desroches​​, J.Jan Sieber​​​‌ and S.Serafim Rodrigues​. Observing hidden neuronal​‌ states in experiments.​​PLoS Computational Biology21​​​‌12December 2025,​ e1013748HALback to​‌ text
• 30 articleE.​​Emre Baspinar, M.​​​‌Martina Simonti, H.​Hadi Srour, M.​‌Mathieu Desroches, D.​​Daniele Avitabile and M.​​​‌Massimo Mantegazza. GABAergic​ neurons can facilitate the​‌ propagation of cortical spreading​​ depolarization: experiments in mouse​​​‌ neocortical slices and a​ novel neural field computational​‌ model.PLoS Computational​​ Biology216June​​​‌ 2025, e1013099HAL​DOIback to text​‌back to textback​​ to text
• 31 article​​​‌D.Damien Depannemaecker,​ F.Federico Tesler,​‌ M.Mathieu Desroches,​​ V.Viktor Jirsa and​​​‌ A.Alain Destexhe.​ Modeling impairment of ionic​‌ regulation with extended Adaptive​​ Exponential integrate-and-fire models.​​​‌Journal of Computational Neuroscience​531March 2025​‌, 1-8HALDOI​​back to text
• 32​​​‌ articleM.Mathieu Desroches​, P.Piotr Kowalczyk​‌ and S.Serafim Rodrigues​​. Discontinuity-induced dynamics in​​​‌ the Conductance-Based Adaptive Exponential​ Integrate-and-Fire Model.Bulletin​‌ of Mathematical Biology87​​1January 2025,​​​‌ 1-23HALDOIback​ to text
• 33 article​‌T.Tamàs Fülöp,​​ A.Alan Cohen,​​ E.Eric Frost,​​​‌ S.Simon Lévesque,‌ A.Abdelouahed Khalil,‌​‌ S.Serafim Rodrigues,​​ M.Mathieu Desroches,​​​‌ M.Mehdi Alami,‌ H.Hicham Berrougui,‌​‌ C.Charles Ramassamy,​​ K.Katsuiku Hirokawa,​​​‌ J.Jacek Witkowski and‌ B.Benoit Laurent.‌​‌ Unmasking the hidden catalyst:​​ how infections trigger Alzheimer's​​​‌ disease.Journal of‌ Alzheimer's DiseaseDecember 2025‌​‌. In press. HAL​​back to text
• 34​​​‌ articleE.Elif Köksal-Ersöz‌, P.Pascal Chossat‌​‌ and F.Frédéric Lavigne​​. Gain modulation of​​​‌ probabilistic selection without synaptic‌ relearning.PLoS ONE‌​‌209September 2025​​, e0333350HALDOI​​​‌back to text
• 35‌ articleL.Louisiane Lemaire‌​‌, M.Mathieu Desroches​​, S.Serafim Rodrigues​​​‌ and F.Fabien Campillo‌. Depolarization block induction‌​‌ via slow NaV1.1 inactivation​​ in Dravet syndrome.​​​‌Scientific Reports151‌September 2025, 32749‌​‌HALDOIback to​​ text
• 36 articleH.​​​‌Hélène Todd, M.‌Mathieu Desroches, A.‌​‌Alex Cayco-Gajic and B.​​Boris Gutkin. The​​​‌ role of gap junctions‌ and clustered connectivity in‌​‌ emergent synchronisation patterns of​​ inhibitory neuronal networks.​​​‌Physical Review E 112‌July 2025, 014405‌​‌HALback to text​​

Conferences without proceedings

• 37​​​‌ inproceedingsE.Emre Baspinar‌. Geometric Neural Fields‌​‌ for Cortical Activity.​​Geometric Science of Information​​​‌16035Lecture Notes in‌ Computer ScienceSaint Malo‌​‌ (Palais du Grand Large),​​ FranceSpringer Nature Switzerland​​​‌October 2025, 183-193‌HALDOIback to‌​‌ textback to text​​back to text

Reports​​​‌ & preprints

• 38 report‌S.Soraya Arias,‌​‌ M.Michel Bergmann,​​ F.Fabien Campillo,​​​‌ M.-A.Marie-Agnès Enard,‌ C.Christian Fabre,‌​‌ F.Frédérick Garcia,​​ B.Benjamin Guedj,​​​‌ E.Emmanuel Jeannot,‌ G.Giovanni Neglia,‌​‌ D.Diane Peurichard,​​ D.Daniel Racoceanu,​​​‌ B.Benoît Sagot and‌ G.Gaëlle Tworkowski.‌​‌ Reflections on the Use​​ of Generative AI for​​​‌ Research Professions.Inria‌July 2025HALback‌​‌ to text
• 39 report​​S.Soraya Arias,​​​‌ M.Michel Bergmann,‌ F.Fabien Campillo,‌​‌ M.-A.Marie-Agnès Enard,​​ C.Christian Fabre,​​​‌ F.Frédérick Garcia,‌ B.Benjamin Guedj,‌​‌ E.Emmanuel Jeannot,​​ G.Giovanni Neglia,​​​‌ D.Diane Peurichard,‌ D.Daniel Racoceanu,‌​‌ B.Benoît Sagot and​​ G.Gaëlle Tworkowski.​​​‌ Réflexions sur l'usage de‌ l'IA générative pour les‌​‌ métiers de la recherche​​.Inria2025,​​​‌ 1-10HAL
• 40 misc‌D.Dmitry Fedorov,‌​‌ F.Fabien Campillo,​​ M.Mathieu Desroches,​​​‌ E.Edgar Soria-Gómez,‌ S.Serafim Rodrigues and‌​‌ J.Joaquin Piriz.​​ Multiple bursting patterns in​​​‌ Lateral Habenula neurons: experiments‌ and computational model.‌​‌January 2025HALDOI​​back to text