Post-Doctoral Research Visit F - M Neural Gain & Adaptive Learning Lenga Project H/F INRIA

Post-Doctoral Research Visit F/M Neural Gain & Adaptive Learning (LENGA Project)
Le descriptif de l'offre ci-dessous est en Anglais
Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

Niveau d'expérience souhaité : De 3 à 5 ans

A propos du centre ou de la direction fonctionnelle

The Inria research centre in Lyon is the 9th Inria research centre, formally created in January 2022. It brings together approximately 300people in 17 research teams and research support services.

Its staff are distributed in Villeurbanne, Lyon Gerland, and Saint-Etienne.

The Lyon centre is active in the fields of software, distributed and high-performance computing, embedded systems, quantum computing and privacy in the digital world, but also in digital health and computational biology.

Contexte et atouts du poste

Cophy is a project team between Inria, Inserm and CRNS, which gathers an international team of researchers, engineers, clinicians and students interested in studying brain networks, to shed light on information processing, its modulation by attention, prediction and learning, as well as the intricate coupling between action and perception. Our research combines (1) cross-species in-vivo observations of brain electrical and neurotransmitter dynamics in health and pathology; (2) in silico models, including Bayesian models, neural mass models and spiking neural networks; (3) in vitro neuronal network measurements. Our aim is to innovate in neurotechnologies in the broadest sense, both for research and for clinical applications, particularly in neurodevelopmental disorders.

Mission confiée

Adaptive behavior depends on selecting advantageous actions while avoiding detrimental ones, a process that requires continuously updating the relationship between actions and outcomes based on experience. In stable environments, such adaptation can rely on gradual adjustments in learning rates, but in dynamic contexts, flexibility demands faster mechanisms that preserve prior knowledge while enabling rapid behavioral change. This raises a fundamental question: how does the brain achieve immediate adaptation without relying solely on slow synaptic modification?

Our recent theoretical and experimantal work explores how dynamic mechanisms operating at the network level may enable rapid behavioral adaptation alongside more traditional forms of learning. This framework seeks to bridge fast, state-dependent computations and slower, experience-driven plasticity, contributing to a more unified understanding of behavioral adaptation.

The project aims to:

- Develop and analyze computational models that capture flexible, multi-timescale learning and adaptation in recurrent neural circuits.
- Test model predictions in behavioral experiments.
- Investigate how principles of biological adaptability can inform the design of efficient and robust learning algorithms for artificial systems.

The candidate will contribute to modeling and analysis of adaptive learning mechanisms, evaluation of their performance across behavioral and computational contexts, and formulation of testable predictions for experimental validation. The recruited person will be in connection with Romain Ligneul and Renato Marciano Maciel from the Cophy Team, and withPascal Chossat(MathNeuro Team, Inria Branch at the Universityof Montpellier) and Frédéric Lavigne (BCL Laboratory, University of Côted'Azur).

References:
- E. Behrens, M. W. Woolrich, M. E. Walton, and M. F. Rushworth, Learning the value of information in an uncertain world, Nature Neuroscience, vol. 10, no. 9, pp. 1214-1221, 2007.
- A. Ferguson and J. A. Cardin, Mechanisms underlying gain modulation in the cortex, Nature Reviews Neuroscience, vol. 21, no. 2, pp. 80-92, 2020.
- D. Grossman and J. Y. Cohen, Neuromodulation and neurophysiology on the timescale of learning and decision-making, Annual Review of Neuroscience, vol. 45, pp. 317-337, 2022.
- Kim, Y. Li, and T. J. Sejnowski, Simple framework for constructing functional spiking recurrent neural networks, PNAS, vol. 116, pp. 22811-22820, 2019.
- Köksal-Ersöz, P. Chossat, and F. Lavigne, Gain modulation of actions selection without synaptic relearning, PLoS ONE, 20(9): e0333350, 2025.
- Mei, E. Muller, and S. Ramaswamy, Informing deep neural networks by multiscale principles of neuromodulatory systems, Trends in Neurosciences, vol. 45, pp. 237-250, 2022.
- Ligneul and Z. F. Mainen, Serotonin, Current Biology, vol. 33, pp. R1216-R1221, 2023.

Principales activités

- Design, implement and optimise learning rules
- Process electrophysiological and behavioural datasets.
- Run numerical simulations to explore different learning timescales and environmental conditions.
- Work closely with the experimental team.
- Writing research papers for submission to top-tier conferences and journals in the field
- Disseminating research findings through presentations at conferences, seminars, and workshops.
- Follow the principals of open-science.

Compétences

- Strong background in recurrent neural networks (ratebased & spiking).
- Prior work on learning algorithms.
- Familiarity with neuromodulatory concepts
- Familarity with dynamical systems
- Experience analysing behavioural or electrophysiological data is a plus.
- Proficiency in Python, especially scientific libraries (NumPy, SciPy) and simulation frameworks (Brian 2, NEST).
- Ability to work autonomously and in interdisciplinary teams.
- Good scientific writing (English) and presentation skills.

Avantages

- Subsidized meals
- Partial reimbursement of public transport costs
- Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
- Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
- Professional equipment available (videoconferencing, loan of computer equipment, etc.)
- Social, cultural and sports events and activities
- Access to vocational training
- Social security coverage

Rémunération

2788 € gross salary / month