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Stephanie Palmer

Associate Professor
sepalmer@uchicago.edu
Websites
Research Summary
I study how populations of neurons collectively encode information present in their inputs and how they perform computations on these signals. The brain performs several classes of computation including signal comparison, prediction, error correction, and learning. To investigate these phenomena, I work with experimentalists on a variety of systems: predictive coding in the retina and visual cortex of the rodent, motion coding in area MT, and temporal coding in the zebra finch song system. From these studies, several general principles have emerged, which guide my current research: the hypothesis that neurons are optimized to predict their future inputs, that information in neural populations is represented combinatorially, and that coding in sensori-motor systems is highly dynamic and behaviorally dependent. By working closely with experimentalists, we constrain and test these theories of neural population coding with detailed measurements.
Education
  • Michigan State University, East Lansing, MI, BS Chemical Physics 05/1997
  • University of Oxford, England, UK, DPhil Theoretical Physics 04/2001
  • University of California San Francisco, San Francisco, CA, Postdoctoral Neuroscience 09/2005
  • Princetion University, Princeton, NJ, Postdoctoral Neuroscience 03/2012
Biosciences Graduate Program Association
Publications

  1. Ding J, Chen A, Chung J, Acaron Ledesma H, Wu M, Berson DM, Palmer SE, Wei W. Spatially displaced excitation contributes to the encoding of interrupted motion by a retinal direction-selective circuit. Elife. 2021 06 07; 10. View in: PubMed

  2. Wang S, Segev I, Borst A, Palmer S. Maximally efficient prediction in the early fly visual system may support evasive flight maneuvers. PLoS Comput Biol. 2021 05; 17(5):e1008965. View in: PubMed

  3. Sachdeva V, Mora T, Walczak AM, Palmer SE. Optimal prediction with resource constraints using the information bottleneck. PLoS Comput Biol. 2021 03; 17(3):e1008743. View in: PubMed

  4. Palmer SE, Wright BD, Doupe AJ, Kao MH. Variable but not random: temporal pattern coding in a songbird brain area necessary for song modification. J Neurophysiol. 2021 02 01; 125(2):540-555. View in: PubMed

  5. Stern M, Arinze C, Perez L, Palmer SE, Murugan A. Supervised learning through physical changes in a mechanical system. Proc Natl Acad Sci U S A. 2020 06 30; 117(26):14843-14850. View in: PubMed

  6. Johnston WJ, Palmer SE, Freedman DJ. Nonlinear mixed selectivity supports reliable neural computation. PLoS Comput Biol. 2020 02; 16(2):e1007544. View in: PubMed

  7. Westerman EL, VanKuren NW, Massardo D, Tenger-Trolander A, Zhang W, Hill RI, Perry M, Bayala E, Barr K, Chamberlain N, Douglas TE, Buerkle N, Palmer SE, Kronforst MR. Aristaless Controls Butterfly Wing Color Variation Used in Mimicry and Mate Choice. Curr Biol. 2018 11 05; 28(21):3469-3474.e4. View in: PubMed

  8. Lombardo JA, Macellaio MV, Liu B, Palmer SE, Osborne LC. State dependence of stimulus-induced variability tuning in macaque MT. PLoS Comput Biol. 2018 10; 14(10):e1006527. View in: PubMed

  9. Sederberg AJ, MacLean JN, Palmer SE. Learning to make external sensory stimulus predictions using internal correlations in populations of neurons. Proc Natl Acad Sci U S A. 2018 01 30; 115(5):1105-1110. View in: PubMed

  10. Zhang W, Westerman E, Nitzany E, Palmer S, Kronforst MR. Tracing the origin and evolution of supergene mimicry in butterflies. Nat Commun. 2017 11 07; 8(1):1269. View in: PubMed

  11. Nitzany EI, Loe ME, Palmer SE, Victor JD. Perceptual interaction of local motion signals. J Vis. 2016 11 01; 16(14):22. View in: PubMed

  12. Sederberg AJ, Palmer SE, MacLean JN. Decoding thalamic afferent input using microcircuit spiking activity. J Neurophysiol. 2015 Apr 01; 113(7):2921-33. View in: PubMed
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