Grants per year
Personal profile
Research Interests
The Fitzgerald Lab is an interdisciplinary group of neurobiologists, physicists, and mathematicians.
"The brain’s fine-scale structure is naturally described in the language of physics, biochemistry, and cellular biology, whereas its large-scale structure is more easily framed by concepts from psychology, machine learning, and artificial intelligence. This is a very exciting time for neuroscience because experimenters are increasingly able to measure circuit-level phenomena that are poised to bridge the gap between low-level and high-level descriptions of the brain. Coordinated theoretical progress is needed to transform these data into hypotheses, theories, and principles of brain function.
"Our research combines first-principles theory, phenomenological modeling, data analysis, and experimental design to build theoretical frameworks and data-driven models that advance the frontiers of neuroscience. We seek a multiscale understanding of the brain and ask questions that link across scales. We also seek general principles, which should illuminate the details of specific systems and direct broad thinking about the brain. Our integrative goals lead us to work on a wide variety of neuroscience problems, brain systems, and animal models. These efforts are closely coordinated with experimental work from collaborators around the world."
Research interest(s):
- Theoretical neuroscience
- Neural basis of behavior
- Whole-brain sensorimotor processing
- Learning and memory
- Neural networks
Education/Academic qualification
Physics, PhD, Stanford University
… → 2013
Physics, MS, Stanford University
… → 2010
Physics, BA, University of Chicago
… → 2007
Mathematics, BS, University of Chicago
… → 2007
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Collaborations and top research areas from the last five years
Grants
- 1 Active
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HHMI Lab Funding Transfer
Fitzgerald, J. E. (PD/PI)
Howard Hughes Medical Institute
1/1/24 → 12/31/28
Project: Research project
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Optimization in Visual Motion Estimation
Clark, D. A. & Fitzgerald, J. E., Apr 25 2024, (E-pub ahead of print) In: Annual Review of Vision Science.Research output: Contribution to journal › Review article › peer-review
1 Scopus citations -
Exact learning dynamics of deep linear networks with prior knowledge
J Dominé, C. C., Braun, L., Fitzgerald, J. E. & Saxe, A. M., Nov 1 2023, In: Journal of Statistical Mechanics: Theory and Experiment. 2023, 11, 114004.Research output: Contribution to journal › Article › peer-review
Open Access -
Organizing memories for generalization in complementary learning systems
Sun, W., Advani, M., Spruston, N., Saxe, A. & Fitzgerald, J. E., Aug 2023, In: Nature neuroscience. 26, 8, p. 1438-1448 11 p.Research output: Contribution to journal › Article › peer-review
Open Access21 Scopus citations -
Reward expectations direct learning and drive operant matching in Drosophila
Rajagopalan, A. E., Darshan, R., Hibbard, K. L., Fitzgerald, J. E. & Turner, G. C., 2023, In: Proceedings of the National Academy of Sciences of the United States of America. 120, 39, e2221415120.Research output: Contribution to journal › Article › peer-review
Open Access3 Scopus citations -
A brainstem integrator for self-location memory and positional homeostasis in zebrafish
Yang, E., Zwart, M. F., James, B., Rubinov, M., Wei, Z., Narayan, S., Vladimirov, N., Mensh, B. D., Fitzgerald, J. E. & Ahrens, M. B., Dec 22 2022, In: Cell. 185, 26, p. 5011-5027.e20Research output: Contribution to journal › Article › peer-review
Open Access12 Scopus citations