Kanaka Rajan

Kanaka Rajan is a computational neuroscientist in the Department of Neurobiology at Harvard Medical School and founding faculty in the Kempner Institute for the Study of Natural and Artificial Intelligence at Harvard University. Rajan trained in engineering, biophysics, and neuroscience, and has pioneered novel methods and models to understand how the brain processes sensory information. 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, and how those processes are affected by various neuropsychiatric disease states. The resulting integrative theories about the brain bridge neurobiology and artificial intelligence.

Early life and education Rajan was born and raised in India. She completed a Bachelors of Technology (B.Tech.) from the Center for Biotechnology at Anna University in Tamil Nadu, India in 2000, majoring in Industrial Biotechnology and graduating with distinction.

In 2002, Rajan pursued a post-graduate degree in neuroscience at Brandeis University, where she did experimental rotations with Eve Marder and Gina G. Turrigiano, before joining Larry Abbott's laboratory where she completed her master's degree (MA).

Doctoral research In Rajan's graduate work, she used mathematical modelling to address neurobiological questions. The main component of her thesis was the development of a theory for how the brain interprets subtle sensory cues within the context of its internal experiential and motivational state to extract unambiguous representations of the external world. This line of work focused on the mathematical analysis of neural networks containing excitatory and inhibitory types to model neurons and their synaptic connections. Her work showed that increasing the widths of the distributions of excitatory and inhibitory synaptic strengths dramatically changes the eigenvalue distributions. In a biological context, these findings suggest that having a variety of cell types with different distributions of synaptic strength would impact network dynamics and that synaptic strength distributions can be measured to probe the characteristics of network dynamics. were employed. Rajan's early, influential work with Abbott and Haim Sompolinsky integrated physics methodology into mainstream neuroscience research — initially by creating experimentally verifiable predictions, and today by cementing these tools as an essential component of the data modelling arsenal. Rajan completed her Ph.D. in 2009. At Princeton, she and her colleagues developed and employed a broad set of tools from physics, engineering, and computer science to build new conceptual frameworks for describing the relationship between cognitive processes and biophysics across many scales of biological organization.

Modelling feature selectivity In Rajan's postdoctoral work with Bialek, she explored an innovative method for modelling the neural phenomenon of feature selectivity. Feature selectivity is the idea that neurons are tuned to respond to specific and discrete components of the incoming sensory information, and later these individual components are merged to generate an overall perception of the sensory landscape. The process, termed "Partial In-Network Training", is used as both model and to match real neural data from the posterior parietal cortex during behavior. To gain insight into fundamental brain processes such as learning, memory, multitasking, or reasoning, Rajan develops theories based on neural network architectures inspired by biology as well as mathematical and computational frameworks that are often used to extract information from neural and behavioral data. Her models are based on experimental data (e.g., calcium imaging, electrophysiology, and behavior experiments) and on new and existing mathematical and computational frameworks derived from machine learning and statistical physics. Rajan continues to apply recurrent neural network modelling to behavioral and neural data. In collaboration with Karl Deisseroth and his team at Stanford University, such models revealed that circuit interactions within the lateral habenula, a brain structure implicated in aversion, were encoding experience features to guide the behavioral transition from active to passive coping – work published in Cell.

In 2019, Rajan was one of twelve investigators to receive funding from the National Science Foundation (NSF) though its participation in the White House's Brain Research through Advancing Innovative Neurotechnologies (BRAIN) Initiative. The same year, she was also awarded an NIH BRAIN Initiative grant (R01) for Theories, Models, and Methods for Analysis of Complex Data from the Brain.

In 2022, Rajan was promoted to Associate Professor with tenure in the Department of Neuroscience and the Friedman Brain Institute at the Icahn School of Medicine at Mount Sinai.

In 2023, Rajan joined the Department of Neurobiology at Harvard Medical School as a Member of the Faculty and Kempner Institute for the Study of Natural and Artificial Intelligence as founding faculty. CIFAR Azrieli Global Scholar – Brain, Mind & Consciousness Program McKnight Scholar Award Next Generation Leaders, Allen Institute for Brain Science (2021) National Science Foundation (NSF) CAREER Award (2021) The Harold & Golden Lamport Basic Research Award (2021) Friedman Brain Institute Research Scholars Award from the DiSabato Family (2019) Sloan Research Fellowship in Neuroscience (2019) Understanding Human Cognition Scholar Award from the James McDonnell Foundation (2016) Visiting Research Fellowship, Janelia Research Campus, Howard Hughes Medical Institute (2016) Brain and Behavior Foundation (formerly, NARSAD) Young Investigator Award (2015-2017) Lectureship, Department of Molecular Biology and the Lewis-Sigler Institute for Integrative Genomics, Princeton University for Methods and Logic in Quantitative Biology (2011-2013) Grant from the Organization for Computational Neurosciences (OCNS) (2011) Sloan-Swartz Theoretical Neuroscience Postdoctoral Fellowship (2010-2012)

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