Alexandre Muzy

This paper introduces Dynamic Structure Learning (DSL), a framework that models how agents adapt their internal structures over time, applied to rats navigating a maze, showing evolving mental maps and learning rules.

This study examines how neural activity in the rat striatum changes during learning, showing distinct coding dynamics in DMS and DLS that correlate with behavioral performance.

This paper introduces a framework to infer agents' internal models from behavior, revealing that suboptimal decisions arise from interactions between learning rules and environment representations in rats.

The paper introduces an algorithm for efficiently simulating large neural networks with time asynchrony, achieving linear computational and memory complexity.