Mick Bonner

Mick Bonner is an Assistant Professor in the Department of Cognitive Science at Johns Hopkins University, where he leads the Bonner Lab. His research lies at the intersection of neuroscience, artificial intelligence, and computational vision, focusing on understanding how visual information is processed in the human brain using neuroimaging and deep learning models.

Research Interests: His work centers on uncovering the statistical principles governing high-dimensional representations in the visual cortex. He investigates how deep neural networks can model brain activity, challenges low-dimensional assumptions in neural coding, and explores hierarchical processing in social vision. His lab employs advanced statistical methods, fMRI, and computational modeling to study neural manifolds, dimensionality, and model-to-brain alignment.

Publication Trends: Recent publications reveal a strong focus on high-dimensional neural representations, universal features across models, and the role of unsupervised learning in building biologically plausible visual hierarchies. His team frequently uses fMRI, naturalistic stimuli, and cross-decomposition techniques to study individual differences and shared cortical codes.

• Glushko Dissertation Prize 2024 – Awarded by the Cognitive Science Society.
• IDIES Seed Funding Award – From Johns Hopkins for innovative data-intensive research.

Advising and Grants: Prof. Bonner advises multiple PhD students, including Emalie McMahon, Raj Magesh Gauthaman, and Ray Chen, whose work has been published in Science Advances, Current Biology, and PLOS Computational Biology. He has secured competitive internal funding and actively mentors students in computational neuroscience. His lab collaborates with institutions like Sakana AI and the Kavli Neuroscience Discovery Institute.

Labs and Teams: The Bonner Lab at Johns Hopkins is an interdisciplinary team combining neuroscience, machine learning, and statistics. They develop open educational resources, including a widely used CCN tutorial on high-dimensional neuroscience, and maintain strong ties with the broader cognitive science and AI research communities.