Max Garagnani
Papers
1
Total Citations
6
H-Index
1
About
Max Garagnani is a leading researcher in computational neuroscience and neurorobotics, whose work bridges the gap between neuroanatomical models and embodied artificial intelligence. His primary research areas include the formation of cell assemblies in large-scale brain networks, multimodal learning, and the integration of biologically plausible neural models with robotic systems. Garagnani’s major contribution lies in demonstrating how distributed neural circuits can learn robust, cross-modal associations—such as linking visual and motor representations—through Hebbian plasticity. His most-cited paper, "Learning visual-motor Cell Assemblies for the iCub robot using a neuroanatomically grounded neural network" (2014, 6 citations), exemplifies this by showing how a neuroanatomically constrained model can enable a humanoid robot to acquire coordinated visual-motor behaviors. This work not only advances our understanding of cortical dynamics but also provides a principled framework for developing more adaptive and brain-inspired autonomous systems. Garagnani’s research has significant implications for both cognitive neuroscience and robotics, offering a powerful tool for exploring how the brain learns and represents complex sensorimotor skills.
Research Focus
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Top Papers
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