Portrait of a smiling woman with short dark hair, wearing a blue denim-textured top and small earrings.

Kanaka Rajan, PhD

Associate Professor of Neurobiology, Harvard Medical School
Rajan Lab

Kanaka Rajan, PhD – People Feature

Associate Professor of Neurobiology in the Department of Neurobiology at Harvard Medical School.

If you prefer, you can view the visual layout directly on Canva by Neuro Coordinator.

Contact

Email: kanaka_rajan@hms.harvard.edu

Rajan Lab website: www.rajanlab.com

The Aim

The Rajan Lab studies how the brain performs essential functions by using neuroscience data to build advanced AI models.

The Impact

Combining biological data with AI tools brings us closer to understanding how different brain regions communicate. For example, we now have a better grasp on what makes multitasking possible and how we learn from just a few experiences. These insights into essential functions could lead to new treatments for disorders affecting memory and learning.

A Closer Look

The Transmitter — March 2025: How neuroscience comics add KA-POW! to the field: Q&A with Kanaka Rajan

This feature shares Kanaka Rajan’s personal and professional insights into how collaborating with artists on comics has helped her convey complex concepts without diluting the science. This approach has allowed her research to reach a larger audience and inspired other researchers to use similar methods.

Harvard Medical School — August 2026: AI Framework Decodes How Brain Regions Talk to One Another

A new AI framework called CURBD reveals how brain regions communicate and influence one another, offering fresh insights into cognition and potential treatments for disorders such as depression, memory loss, and movement disorders.

Publications View
Eigenvalue spectra of random matrices for neural networks.
Authors: Authors: Rajan K, Abbott LF.
Phys Rev Lett
View full abstract on Pubmed
Neural network dynamics.
Authors: Authors: Vogels TP, Rajan K, Abbott LF.
Annu Rev Neurosci
View full abstract on Pubmed
arXiv
Authors: Authors: A brain basis of dynamical intelligence for AI and computational neuroscience
2021.
arXiv
Authors: Authors: Efficient and robust multi-task learning in biological brains with modular task primitives
2022.
Nature Machine Intelligence
Authors: Authors: A ‘programming’ framework for recurrent neural networks
2023; (5):570-571.
bioRxiv
Authors: Authors: Inferring brain-wide interactions using data-constrained recurrent neural network models
2021.
bioRxiv
Authors: Authors: Reservoir-based Tracking (TRAKR) For One-shot Classification Of Neural Time-series Patterns
2022.
The Computational Brain
Authors: Authors: The Frontiers of Medical Research: Brain Science (Science/AAAS)
2023; 19-20.
bioRxiv
Authors: Authors: Contributions and synaptic basis of diverse cortical neuron responses to task performance
2022.
International Conference on Learning Representations
Authors: Authors: Curriculum learning as a tool to uncover learning principles in the brain
2022.