Max Grogan
Papers
2
Total Citations
6
H-Index
2
About
Max Grogan is a computational neuroscientist whose work focuses on unraveling the neural basis of proprioception—the sense of body position and movement, one of the least understood sensory systems. His major contribution is the development of the topographic variational autoencoder (topo-VAE), a sophisticated model that integrates lateral connectivity to predict how limb pose is represented in the somatosensory cortex. By bridging machine learning and neurobiology, Grogan’s research offers a powerful framework for inferring cortical anatomy and neural coding principles from behavioral data alone. His most-cited paper (2021, 4 citations) and its follow-up (2024, 2 citations) have laid foundational groundwork for understanding how the brain constructs a continuous, topographic map of the body’s position—a challenge that has long eluded experimental approaches. Though still early in his career, Grogan’s innovative use of autoencoders to model proprioceptive processing marks a significant step forward in sensory neuroscience, with implications for brain-machine interfaces and rehabilitation. His work exemplifies how computational models can illuminate the hidden logic of the brain’s internal representations.
Research Focus
Key Achievements
Top Papers
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