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 with Pascal Chossat(MathNeuro Team, Inria Branch at the University of Montpellier) and Frédéric Lavigne (BCL Laboratory, University of Côte d’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.
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