November 21, 2025, Webb Hall 1100

The hippocampus, the navigation center of the mammalian brain, can be thought of as a state estimator that integrates self-motion signals over time and corrects error using measurements of external landmarks. Using a custom virtual reality apparatus, we discovered that the integration process is highly plastic and can be recalibrated using feedback from landmarks (Jayakumar et al., Nature, 2019) or optic flow cues (Madhav et al., Nat Neurosci, 2024). Using a biophysically plausible attractor neural network model of path integration, we proved that, for landmark-based recalibration, the path integration error (or its integral) must be encoded at the level of individual neurons to enable recalibration (Secer et al., Nat Commun, 2025). Guided by this prediction, we revisited the physiological data and (1) discovered a rate code for error at the level of individual neurons and (2) showed that the degree of recalibration can be predicted from network dynamics during the recalibration process.

Noah J. Cowan is a professor in the Department of Mechanical Engineering at Johns Hopkins University, Baltimore, MD. Prof. Cowan’s research interests include mechanics and multisensory control in animals (including humans) and machines, and he has published articles in a range of fields, from control systems and robotics to neuroscience and biomechanics. Prof. Cowan received the NSF PECASE Award in 2010, the James S. McDonnell Foundation Scholar Award in Complex Systems in 2012, the William H. Huggins Award for Excellence in Teaching in 2004, and Johns Hopkins University Discovery Awards in 2015, 2016, and 2023. He is a Fellow of the IEEE.