Pocket-Sized AI Brain: Monkey Neurons Model | AcademicJobs
US researchers at CSHL, Princeton, and CMU unveil a tiny AI model mimicking monkey V4 neurons, slashing parameters 5000x while matching performance—paving way for efficient bio-inspired AI.
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Benjamin Cowley is an Assistant Professor in Neuroscience at Cold Spring Harbor Laboratory, where he has served since 2022. He earned a B.S. in Electrical and Computer Engineering from Carnegie Mellon University in 2012 and a Ph.D. in Machine Learning from the School of Computer Science at Carnegie Mellon University in 2018. From 2018 to 2022, he was a postdoctoral researcher at the Princeton Neuroscience Institute, working with advisors Jonathan Pillow and Mala Murthy.
Cowley leads the Cowley group, which develops machine learning techniques, including deep learning and active learning, to identify data-driven models of neural responses and behavior through closed-loop experiments. His research focuses on creating compact, interpretable models of computations in the brain, particularly in visual systems of primates and fruit flies. Key publications include “Mapping model units to visual neurons reveals population code for social behaviour” in Nature (2024), “Time to let the model speak for itself with closed-loop neurophysiology” in Nature Reviews Neuroscience (2025), and earlier works in Neuron and PLoS Computational Biology. He has contributed to advancing understanding of neural population codes and dimensionality reduction in neural activity.
US researchers at CSHL, Princeton, and CMU unveil a tiny AI model mimicking monkey V4 neurons, slashing parameters 5000x while matching performance—paving way for efficient bio-inspired AI.
Discover how researchers at CSHL, Princeton, and CMU compressed a massive AI model using macaque V4 neuron data into a pocket-sized powerhouse, revolutionizing neuroscience and AI.