Papers
16
Total Citations
224
H-Index
8
About
Francesco Mannella is a leading computational neuroscientist whose research bridges the gap between brain-inspired learning mechanisms and autonomous robotics. His primary focus lies in understanding how biological systems—particularly the basal ganglia, amygdala, and dopaminergic pathways—drive intrinsically motivated, open-ended learning. Mannella’s most influential work, “Intrinsically motivated action–outcome learning and goal-based action recall” (2012, 54 citations), provides a system-level bio-constrained model that explains how organisms learn from the consequences of their own actions. He further advanced the field with a landmark study on striatal dopamine (2014, 40 citations), proposing a unified mechanism for both phasic and tonic dopamine functions, validated in a simulated humanoid robot. His research on body-awareness through intrinsic goals (2018, 27 citations) offers a computational framework for how infants autonomously build sensorimotor maps, with direct applications to developmental robotics. Mannella has also modeled the interplay of Pavlovian and instrumental conditioning (2010, 21 citations) and contributed to active inference approaches for motor control (2023). His work within the ESA-funded IMPACT project demonstrates the real-world relevance of his curiosity-driven architectures for space robotics. With over 200 total citations, Mannella’s research is essential reading for anyone interested in bio-inspired learning, intrinsic motivation, and the neural foundations of goal-directed behavior.
Research Focus
Key Achievements
Top Papers
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- 3Know Your Body Through Intrinsic Goals27 citations · 2018
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- 6Modeling Motor Control in Continuous Time Active Inference: A Survey14 citations · 2023
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