I am a postdoc at Carnegie Mellon University, working with Dr. Leila Wehbe and Dr. Michael Tarr and funded by a Distinguished Postdoctoral Fellowship from the Carnegie Mellon Neuroscience Institute. My research combines techniques from neuroscience and machine learning, aiming to understand the computational basis of human visual cognition. I am particularly interested in the relationship between low-level visual representations and high-level semantic category representations in the brain, and how it reflects the correlational structure of the visual environment.
Previously, I obtained my PhD in Neurosciences with a Specialization in Computational Neurosciences from the University of California, San Diego, working with Dr. John Serences. During my PhD I used functional magnetic resonance imaging (fMRI) along with computational modeling to investigate how adaptive neural codes support cognitive abilities like short-term memory and object recognition. I also used neural network models to explore how natural image statistics contribute to biases in visual feature representations.
View my full CV here.
Publications and pre-prints
Henderson, M.M., Tarr, M.J., & Wehbe, L. (2023). A texture statistics encoding model reveals hierarchical feature selectivity across human visual cortex. Journal of Neuroscience. (pdf)
Henderson, M.M., Tarr, M.J., & Wehbe, L. (2023). Low-level tuning biases in higher visual cortex reflect the semantic informativeness of visual features. Journal of Vision. (pdf)
Luo, A.F., Wehbe, L., Tarr, M.J., & Henderson, M.M. (2023). Neural Selectivity for Real-World Object Size in Natural Images. bioRxiv; under review.
Jain, N., Wang, A., Henderson, M.M., Lin, R., Prince, J.S., Tarr, M.J., & Wehbe, L. (2023). Selectivity for food in human ventral visual cortex. Communications Biology. (pdf)
Jinsi, O.* , Henderson, M.M.*, & Tarr, M.J. (2023). Early experience with low-pass filtered images facilitates visual category learning in a neural network model. PLOS ONE. (pdf)
Henderson, M.M., Rademaker, R.L., & Serences, J.T. (2022). Flexible utilization of spatial- and motor-based codes for the storage of visuo-spatial information. eLife. (pdf)
Henderson, M.M., & Serences, J.T. (2021). Biased orientation representations can be explained by experience with non-uniform training set statistics. Journal of Vision. (pdf)
Henderson, M.M.* , Vo, V.A.* , Chunharas, C., Sprague, T.C., & Serences, J.T. (2019). Multivariate analysis of BOLD activation patterns recovers graded depth representations in human visual and parietal cortex. eNeuro. (pdf)
Henderson, M.M. & Serences, J.T. (2019). Human frontoparietal cortex represents behaviorally relevant target status based on abstract object features. Journal of Neurophysiology. (pdf)
Henderson, M.M., Gardner, J., Raguso, R.A., & Hoffman, M.P. (2017). Trichogramma ostriniae (Hymenoptera: Trichogrammatidae) response to relative humidity with and without host cues. Biocontrol Science and Technology.
Henderson, M.M., Pinskiy, V., Tolpygo, A., Savoia, S., Grange, P., & Mitra, P. (2014). Automated placement of stereotactic injections using a laser scan of the skull. arXiv.
*These authors made equal contributions.
mmhender [at] cmu [dot] edu