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| Timothy Behrens | |
|---|---|
| Name | Timothy Behrens |
| Nationality | British |
| Fields | Neuroscience |
| Workplaces | University of Oxford |
| Alma mater | University of Cambridge |
| Known for | Computational neuroimaging |
Timothy Behrens is a British neuroscientist who has made influential contributions to cognitive neuroscience, computational neuroimaging, and the study of decision-making. He has held research and teaching positions at leading institutions including the University of Oxford and University College London, and has collaborated with investigators across the neuroscience, psychology, and artificial intelligence communities. His work integrates methods from computational modeling, functional magnetic resonance imaging, and electrophysiology to probe neural representations and learning in humans and animals.
Behrens was educated in the United Kingdom, studying at the University of Cambridge and the University of Oxford, where he trained in neuroscience and computational approaches. During his doctoral and postdoctoral years he worked with prominent figures in cognitive science and neurophysiology, collaborating with researchers connected to institutions such as the Wellcome Trust, Medical Research Council, and the Howard Hughes Medical Institute. His formative mentors and collaborators include scientists associated with the University of Cambridge, the University of Oxford, and University College London, linking him to broader networks that involve researchers from the Massachusetts Institute of Technology, Harvard University, and the Max Planck Society.
Behrens has held appointments at multiple research centers and departments, including roles at the Wellcome Trust Centre for Neuroimaging, the Department of Experimental Psychology at the University of Oxford, and faculties connected to University College London. He has served as a principal investigator directing labs that bridge computational modeling and human neuroimaging, and has taught courses drawing on curricula from Oxford and Cambridge graduate programs. His academic collaborations span partnerships with investigators at institutions such as University College London, the University of Cambridge, Harvard Medical School, Stanford University, and the École Normale Supérieure, fostering interdisciplinary projects in neural coding, reinforcement learning, and systems neuroscience.
Behrens's research focuses on how the brain represents information and supports learning, decision-making, and flexible behavior. He has developed and applied computational models inspired by reinforcement learning and Bayesian inference to interpret human and animal neural data recorded with functional magnetic resonance imaging, electrophysiology, and calcium imaging. His investigations often examine structures such as the hippocampus, prefrontal cortex, striatum, and parietal cortex, situating findings within frameworks associated with researchers at institutes like the Max Planck Institute, Cold Spring Harbor Laboratory, and the Salk Institute.
Notable conceptual contributions include advances in representational similarity analysis and multivariate pattern analysis that link neural patterns to cognitive maps, episodic memory, and spatial navigation—concepts connected to work by investigators at Columbia University, New York University, and Johns Hopkins University. Behrens has explored how uncertainty, prediction error, and hierarchical inference modulate neural coding, integrating ideas related to the Bayesian brain hypothesis promoted by scholars at University College London and Imperial College London. His collaborative studies have bridged human neuroimaging and rodent electrophysiology, aligning with experimental traditions from the University of California, Berkeley, Princeton University, and ETH Zurich.
Methodologically, he has contributed to the development of tools and pipelines for preprocessing and analyzing functional MRI data, complementing software efforts from the Neuroinformatics community such as those associated with the Allen Institute for Brain Science, the Human Connectome Project, and the International Neuroinformatics Coordinating Facility. His lab's empirical work has addressed topics including model-based versus model-free reinforcement learning, neural substrates of value representation, and the dynamics of learning under volatility—areas also investigated by groups at Yale University, the University of Michigan, and the National Institutes of Health.
Behrens has received recognition from several funding and award bodies, including support from the Wellcome Trust and the Medical Research Council. His work has been cited in forums and symposia organized by international societies and academies such as the Royal Society, the Society for Neuroscience, the British Neuroscience Association, and the European Brain and Behaviour Society. He has been invited to present lectures at venues including the Royal Institution, the Kavli Foundation, and university colloquia at institutions like Oxford, Cambridge, Harvard, and Stanford.
- Behrens T. (selected author). Studies on computational neuroimaging linking hippocampal representations to cognitive maps, published in journals frequented by contributors from University College London, Harvard Medical School, and Columbia University. - Behrens T. (selected author). Papers on representational similarity analysis and multivariate pattern approaches, appearing alongside work from the Max Planck Institute, University of Cambridge, and Princeton University. - Behrens T. (selected author). Articles on hierarchical Bayesian models of learning and decision-making, connecting to theory developed at Imperial College London, University College London, and ETH Zurich. - Behrens T. (selected author). Research integrating human fMRI and animal electrophysiology addressing prediction error and uncertainty, in collaboration with investigators from Yale University, University of California, Berkeley, and the Salk Institute.
Category:British neuroscientists