Laureline Logiaco
Columbia University, Institute of Cognitive and Brain Sciences
Papers
2
Total Citations
6
H-Index
2
About
Laureline Logiaco is a computational neuroscientist whose research lies at the intersection of motor control, neural circuit dynamics, and machine learning. Her work focuses on understanding how the brain—particularly thalamocortical circuits—learns and executes complex, hierarchical sequences of movement. In her highly cited 2020 study, Logiaco investigated how recurrent neural networks can learn to concatenate reusable "motor primitives" or motifs, offering a powerful framework for both neuroscience and robotics. This work provides critical insights into how the brain might achieve robust, flexible control over complex behaviors. Her contributions have been recognized within the broader motor control community; she co-authored a 2024 conference report highlighting the latest debates and discoveries in the field, including foundational mechanisms of motor control. With her papers garnering attention and citations, Logiaco is establishing herself as a leading voice in the study of neural dynamics and hierarchical motor learning, bridging theoretical models with biological plausibility to advance our understanding of how the brain orchestrates movement.
Research Focus
Key Achievements
Top Papers
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- 2