A fundamental goal of neuroscience to link structure and function, and modern experimental techniques bring this goal closer to reach. In the last decade, the floodgates of neural data have opened: the activity of huge populations of neurons can be monitored simultaneously; single-cell sequencing reveals the richness of anatomical cell types; heroic mapping projects reveal motifs of connectivity with unprecedented detail.
But as vast as these datasets are, they are nowhere near enough to write down detailed, predictive mechanistic models of entire circuits. Therefore, we ask somewhat different questions. What are the minimal ingredients to a model necessary to explain the neuronal dynamics observed in an experiment? In what significant ways do the dynamics of neuronal activity vary, across individuals, brain areas, and species, and how do these dynamics support complex behavior? The Sederberg group uses theoretical modeling and advanced analytic methods to answer these questions, working in close collaboration with experimental groups.
- Sederberg, A., & Nemenman, I. (2020). Randomly connected networks generate emergent selectivity and predict decoding properties of large populations of neurons. PLOS Computational Biology, 16(5), e1007875.
- Morrell, M. C., Sederberg, A. J., & Nemenman, I. (2021). Latent Dynamical Variables Produce Signatures of Spatiotemporal Criticality in Large Biological Systems. Physical review letters, 126(11), . https://doi.org/10.1103/PhysRevLett.126.118302
- Sederberg A., Pala A, Zheng HJV, He BJ, Stanley GB. (2019) State-aware detection of sensory stimuli in the cortex of the awake mouse. PLoS Comput Biol. 15 (5):e1006716. DOI: 10.1371/journal.pcbi.1006716
- Sederberg, A., MacLean, J.N., and Palmer, S.E. (2018) Learning to make external sensory stimulus predictions using internal correlations in populations of neurons. PNAS 115 (5), 1105-1110. DOI: 10.1073/pnas.1710779115