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Olivier Codol

Postdoctorat - UdeM
Superviseur⋅e principal⋅e
Guillaume Lajoie
Co-supervisor
Matt Perich
Sujets de recherche
Apprentissage par renforcement
Neurosciences computationnelles
Réseaux de neurones récurrents
Systèmes dynamiques
Google Scholar
Bluesky
GitHub

Publications

Brain-like learning with exponentiated gradients
Jonathan Cornford
Roman Pogodin
Arna Ghosh
Kaiwen Sheng
Brendan A. Bicknell
Olivier Codol
Beverley A. Clark
Guillaume Lajoie
Blake Richards
2024-10-26
bioRxiv (prépublication)
doi.org
Brain-like neural dynamics for behavioral control develop through reinforcement learning
Olivier Codol
Nanda H Krishna
Guillaume Lajoie
M.G. Perich
During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produ… (voir plus)ce neural dynamics that enable the rich repertoire of behaviors we perform with our limbs. What begins as a series of “babbles” coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories—supervised learning (SL) and reinforcement learning (RL)—that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that the similarity between RL-trained networks and biological brains depends critically on matching biomechanical properties of the limb. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.
2024-10-06
bioRxiv (prépublication)
doi.org
Brain-like neural dynamics for behavioral control develop through reinforcement learning
Olivier Codol
Nanda H Krishna
Guillaume Lajoie
M.G. Perich
During development, neural circuits are shaped continuously as we learn to control our bodies. The ultimate goal of this process is to produ… (voir plus)ce neural dynamics that enable the rich repertoire of behaviors we perform with our limbs. What begins as a series of “babbles” coalesces into skilled motor output as the brain rapidly learns to control the body. However, the nature of the teaching signal underlying this normative learning process remains elusive. Here, we test two well-established and biologically plausible theories—supervised learning (SL) and reinforcement learning (RL)—that could explain how neural circuits develop the capacity for skilled movements. We trained recurrent neural networks to control a biomechanical model of a primate arm using either SL or RL and compared the resulting neural dynamics to populations of neurons recorded from the motor cortex of monkeys performing the same movements. Intriguingly, only RL-trained networks produced neural activity that matched their biological counterparts in terms of both the geometry and dynamics of population activity. We show that the similarity between RL-trained networks and biological brains depends critically on matching biomechanical properties of the limb. We then demonstrated that monkeys and RL-trained networks, but not SL-trained networks, show a strikingly similar capacity for robust short-term behavioral adaptation to a movement perturbation, indicating a fundamental and general commonality in the neural control policy. Together, our results support the hypothesis that neural dynamics for behavioral control emerge through a process akin to reinforcement learning. The resulting neural circuits offer numerous advantages for adaptable behavioral control over simpler and more efficient learning rules and expand our understanding of how developmental processes shape neural dynamics.
2024-10-06
bioRxiv (prépublication)
doi.org