Kanaka Rajan, PhD
About Me
Kanaka Rajan, Ph.D. is a Computational Neuroscientist and Associate 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.
In the News: Dr. Rajan discusses the benefit of using recurrent neural networks to model and study the brain in “To Be Energy-Efficient, Brains Predict Their Perceptions” in Quanta Magazine. View the article: https://www.quantamagazine.org/to-be-energy-efficient-brains-predict-their-perceptions-20211115/
Language
Position
Multi-Disciplinary Training Areas
Neuroscience [NEU]
About Me
Kanaka Rajan, Ph.D. is a Computational Neuroscientist and Associate 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.
In the News: Dr. Rajan discusses the benefit of using recurrent neural networks to model and study the brain in “To Be Energy-Efficient, Brains Predict Their Perceptions” in Quanta Magazine. View the article: https://www.quantamagazine.org/to-be-energy-efficient-brains-predict-their-perceptions-20211115/
Language
Position
Multi-Disciplinary Training Areas
Neuroscience [NEU]
