Anna Schapiro

Assistant Professor (starting July 2019)

B.S., Symbolic Systems, Stanford University

Ph.D., Psychology and Neuroscience, Princeton University


Office Location: 
Levin Building, 425 S. University Ave.
Research Interests: 

Memory; Learning; Neural network modeling; Sleep; Consolidation; Hippocampal-cortical interactions

Research Synopsis:

Some moments in our lives provide information that is useful in itself (learning someone’s name), but other information is most meaningful when combined across many instances, as we come to understand the regularities in the world (which friends we can rely on). How do we extract such structured knowledge from our environment? Answering this question requires an understanding of the initial acquisition of this information as well as its stabilization and integration with existing knowledge structures over time and with sleep. Our research combines neural network modeling and empirical methods (fMRI, EEG, patient studies, behavior) to uncover learning algorithms and principles of how memories of regularities in the environment come to be represented throughout the brain.

Professor Anna Schapiro will be considering new graduate students for admission for Fall 2019.


Selected Publications: 

Schapiro, A.C., McDevitt, E.A., Rogers, T.T., Mednick, S.C., & Norman, K.A. (2018). Human hippocampal replay during rest prioritizes weakly-learned information and predicts memory performance. Nature Communications.

Honey, C.J., Newman, E.L., & Schapiro, A.C. (2017). Switching between internal and external modes: a multi-scale learning principle. Network Neuroscience.

Schapiro, A.C., McDevitt, E.A., Chen, L., Norman, K.A., Mednick, S.C., & Rogers, T.T. (2017). Sleep benefits memory for semantic category structure while preserving exemplar-specific information. Scientific Reports. 

Schapiro, A.C., Turk-Browne, N.B., Botvinick, M.M., & Norman, K.A. (2017). Complementary learning systems within the hippocampus: A neural network modelling approach to reconciling episodic memory with statistical learning. Philosophical Transactions of the Royal Society B.

Schapiro, A.C., Turk-Browne, N.B., Norman, K.A., & Botvinick, M.M. (2016). Statistical learning of temporal community structure in the hippocampus. Hippocampus.

Schapiro, A.C., Gregory, E., Landau, B., McCloskey, M., Turk-Browne, N.B. (2014). The necessity of the medial temporal lobe for statistical learning. Journal of Cognitive Neuroscience.

Schapiro, A.C., Rogers, T.T., Cordova, N.I., Turk-Browne, N.B., & Botvinick, M.M. (2013). Neural representations of events arise from temporal community structure. Nature Neuroscience.

Schapiro, A.C., Kustner, L.V., & Turk-Browne, N.B. (2012). Shaping of object representations in the human medial temporal lobe based on temporal regularities. Current Biology.