Kanaka Rajan, Ph.D. is a Computational Neuroscientist and Assistant Professor at the Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai in New York. Her research seeks to understand how important cognitive functions — such as learning, remembering, and deciding — emerge from the cooperative activity of multi-scale neural processes. Using data from neuroscience experiments, Kanaka applies computational frameworks derived from machine learning and statistical physics to uncover integrative theories about the brain that bridge neurobiology and artificial intelligence.
Before joining the faculty at Mount Sinai, Kanaka completed her postdoctoral work at Princeton University, where she made significant contributions to the modeling of important neural processes, including feature selectivity with Dr William Bialek and neural network models inspired by biology with Dr David Tank. She received her Ph.D. at Columbia University with Dr. Larry Abbott.
Multi-Disciplinary Training Area
Co-Lead for the Computational Neuroscience Working Group within Interagency Modeling and Analysis Group (IMAG)
National Institutes of Health
Friedman Brain Institute Research Scholars Award from the Dyal Foundation
Friedman Brain Institute Research Scholars Award from the DiSabato Family
Sloan Research Fellowship in Neuroscience
BRAIN Initiative R01, Theories, Models, and Methods pathway, National Institutes of Biomedical Engineering and Bioimaging
National Institutes of Health (NIBIB/NIH)
FOUNDATIONS Award from the National Science Foundation (NSF)
Mindlin Foundation 1Tweet1P Award, Neuroscience meets Graphic Novel
Understanding Human Cognition Scholar Award from the James McDonnell
Visiting Research Fellowship, Janelia Research Campus, Howard Hughes Medical Institute
Brain and Behavior Foundation (formerly, NARSAD) Young Investigator Award
Lectureship, Department of Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics, Princeton University for Methods and Logic in Quantitative Biology
Grant from the Organization for Computational Neurosciences (OCNS)
Sloan-Swartz Theoretical Neuroscience Postdoctoral Fellowship
Pulin Sampat Memorial Teaching Award, Brandeis University
Tata Institute of Fundamental Research Junior Research Fellowship
The Rajan Lab brings together the fields of brain research and AI to figure out how the brain works. We use mathematical and computational models based on data collected from neuroscience experiments to design an artificial system that can perform realistic behaviors using only the machinery the biological nervous system has access to (i.e., neurons and synapses operating at a fast timescale). After building these systems, we can then ‘reverse engineer’ them to reveal the operating principles of the real brain.
The resulting integrative theories and models have the potential to transform the way we study the brain, by making specific, quantifiable predictions that lead to new hypotheses about how the brain works. We are currently applying this approach to both healthy brains and to those affected by neuropsychiatric diseases. For more information about the Rajan Lab, please visit www.rajanlab.com.
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Dr.Rajan did not report having any of the following types of financial relationships with industry during 2020 and/or 2021: consulting, scientific advisory board, industry-sponsored lectures, service on Board of Directors, participation on industry-sponsored committees, equity ownership valued at greater than 5% of a publicly traded company or any value in a privately held company. Please note that this information may differ from information posted on corporate sites due to timing or classification differences.
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