Portrait of Maxime Daigle is unavailable

Maxime Daigle

Master's Research - McGill University
Supervisor
Pouya Bashivan
Research Topics
Brain-inspired AI
Computational Neuroscience
Deep Learning
Memory
NeuroAI
Neuroscience
Neurotechnology
Reinforcement Learning

Publications

Spatially and non-spatially tuned hippocampal neurons are linear perceptual and nonlinear memory encoders
Maxime Daigle
Kaicheng Yan
Benjamin Corrigan
Roberto Gulli
Julio Martinez-Trujillo
Pouya Bashivan
2025-06-24
bioRxiv (preprint)
doi.org
Building spatial world models from sparse transitional episodic memories
Zizhan He
Maxime Daigle
Pouya Bashivan
2025-05-19
ArXiv (preprint)
arxiv.org
arxiv.org
Building spatial world models from sparse transitional episodic memories
Zizhan He
Maxime Daigle
Pouya Bashivan
Many animals possess a remarkable capacity to rapidly construct flexible mental models of their environments. These world models are crucial… (see more) for ethologically relevant behaviors such as navigation, exploration, and planning. The ability to form episodic memories and make inferences based on these sparse experiences is believed to underpin the efficiency and adaptability of these models in the brain. Here, we ask: Can a neural network learn to construct a spatial model of its surroundings from sparse and disjoint episodic memories? We formulate the problem in a simulated world and propose a novel framework, the Episodic Spatial World Model (ESWM), as a potential answer. We show that ESWM is highly sample-efficient, requiring minimal observations to construct a robust representation of the environment. It is also inherently adaptive, allowing for rapid updates when the environment changes. In addition, we demonstrate that ESWM readily enables near-optimal strategies for exploring novel environments and navigating between arbitrary points, all without the need for additional training.
2025-05-01
arXiv (published)
doi.org
arxiv.org