Spatial Navigation using Bio-Plausible Learning Rules
Spatial navigation is a fundamental cognitive ability that allows organisms to explore, learn, and adapt within their environments. In neuroscience, studies of the hippocampus and grid cells reveal how biological systems encode spatial maps for efficient navigation. Reinforcement learning (RL), inspired by reward-based decision-making, provides a computational framework for modeling these behaviors. Bio-plausible RL combines insights from neuroscience with RL algorithms to mimic natural learning processes, emphasizing local learning rules, neural dynamics, and memory-based strategies. Applying this approach to spatial navigation enables the development of intelligent agents that learn robust, adaptable paths while offering deeper understanding of brain-inspired computation.
I'm a Computer Science student at EPFL with a strong foundation in mathematics, physics, and interdisciplinary thinking. My academic path includes distinctions such as the Laidlaw Leadership and Research Scholarship and hands-on research experience in computational neuroscience, where I explore biologically plausible reinforcement learning.
Beyond academics, I have practical experience ranging from electronics diagnostics to web development for social initiatives. I'm passionate about educational outreach—leading workshops for children, tutoring young students, and promoting inclusive learning. I’m also active in sports and the arts as a football player, pianist, and chess enthusiast.
With fluency in Italian, French, and English, and a growing command of German, I enjoy connecting across cultures and disciplines to solve meaningful problems.
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